[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"_public_publisher_byId_4b00bc14-5b48-46b5-8289-d46112eb0757":3,"_public_publication_all{\"sortAscending\":false,\"sortField\":\"updateTime\",\"page\":0,\"size\":10,\"facet\":true,\"searchKey\":\"publisherId:4b00bc14-5b48-46b5-8289-d46112eb0757,\"}":114},{"code":4,"data":5,"meta":24},"SUCCESS",{"id":6,"createTime":7,"updateTime":8,"relativeEntities":9,"slug":10,"properties":11,"entityType":22,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25,"subjectFields":26,"manageAffiliations":35,"indexDatabases":57,"url":97,"thumbnailPath":24,"statistic":98,"gsStatistic":24,"type":24,"analyzePriority":24},"4b00bc14-5b48-46b5-8289-d46112eb0757","2023-05-29T10:25:38.738+00:00","2025-11-21T09:59:01.032+00:00",[],"Egyptian-Journal-of-Remote-Sensing-and-Space-Science",{"country":12,"issn":14,"introduce":16,"eissn":18,"title":20},{"VOID":13},"EG",{"VOID":15},"11109823",{"EN":17},"The Egyptian Journal of Remote Sensing and Space Sciences (EJRS) covers all aspects of Remote Sensing, Geographic Information Systems and the development of Space Technologies and Applications. The objective of EJRS is to publish on the development of remote sensing technology and its applications for optimal planning, sustainable development and the protection of the environmental resources.",{"VOID":19},"20902476",{"EN":21},"Egyptian Journal of Remote Sensing and Space Science","PUBLISHER","PENDING",null,0,[27],{"id":28,"createTime":29,"updateTime":30,"relativeEntities":31,"label":32,"description":34,"parentId":24,"standard":24,"scholarHubFieldId":24},"cd0e9c62-9445-4f11-88d8-ffaaa645b234","2023-05-29T10:24:10.509+00:00","2023-11-21T07:57:34.083+00:00",[],{"EN":33},"Earth and Planetary Sciences (miscellaneous)",{},[36,47],{"id":37,"createTime":38,"updateTime":39,"relativeEntities":40,"slug":41,"properties":42,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":45,"url":24,"parentIds":46,"statistic":24},"c749757b-dddf-4e6f-9697-b9c441adc06c","2023-05-29T10:24:07.401+00:00","2025-11-21T10:06:14.206+00:00",[],"Elsevier",{"title":43},{"EN":41},"AFFILIATION",11,[],{"id":48,"createTime":49,"updateTime":50,"relativeEntities":51,"slug":52,"properties":53,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25,"url":24,"parentIds":56,"statistic":24},"29dac199-4147-448a-a283-9a89af849242","2023-05-29T10:25:38.694+00:00","2023-12-20T19:06:27.248+00:00",[],"National-Authority-for-Remote-Sensing-and-Space-Sciences",{"title":54},{"EN":55},"National Authority for Remote Sensing and Space Sciences",[],[58,77],{"id":59,"indexDatabase":60,"url":72,"indexYears":73,"academicFieldIds":74,"indexDatabaseRanking":76},"155ec21f-b59d-4e36-9d5b-fc4dac8633a0",{"id":61,"createTime":62,"updateTime":63,"relativeEntities":64,"label":65,"description":67,"key":69,"publicationTags":70,"standard":24},"3c7051d4-eb7d-4c57-a56b-36fc74c5d1e9","2023-05-22T09:57:18.509+00:00","2025-11-21T10:07:52.274+00:00",[],{"EN":66,"VI":66},"Scopus - Elsevier",{"EN":66,"VI":68},"Cơ sở dữ liệu Scopus thuộc Elsevier","scopus",[71],"SCOPUS","https:\u002F\u002Fwww.scopus.com\u002Fsourceid\u002F19700183014","2003,2010-2025",[75],"1689391c-5702-4349-aaa7-d720ee4321fc","SCOPUS__Q1",{"id":78,"indexDatabase":79,"url":93,"indexYears":24,"academicFieldIds":94,"indexDatabaseRanking":24},"a8ad1e5d-5317-462f-90ae-30fd91d58dc1",{"id":80,"createTime":81,"updateTime":82,"relativeEntities":83,"label":84,"description":86,"key":89,"publicationTags":90,"standard":24},"a4921856-b128-4d9f-8f1f-e80813d3bbd4","2023-05-22T09:59:31.026+00:00","2025-11-21T10:07:52.153+00:00",[],{"EN":85,"VI":85},"ISI\u002FSCIE - Science Citation Index Expanded",{"VI":87,"EN":88},"Cơ sở dữ liệu SCIE","SCIE database","scie",[91,92],"SCIE","ISI","https:\u002F\u002Fmjl.clarivate.com\u002Fsearch-results?issn=1110-9823",[95,96],"f16e477b-fe13-47eb-901a-eb56971008f7","d35f7cb1-70f1-41cc-b01c-ebcc9f6a923d","https:\u002F\u002Fwww.journals.elsevier.com\u002Fthe-egyptian-journal-of-remote-sensing-and-space-sciences\u002F",{"impactFactor":25,"impactFactorByYear":99,"i10Index":25,"i10IndexLast5Year":25,"totalPublication":100,"totalPublicationByYear":101,"totalCitation":25,"totalCitationByYear":112,"totalCitationPerPublication":25,"totalCitationPerPublicationByYear":113,"hindexLast5Year":25,"hindex":25},{},133,{"2010":102,"2011":103,"2012":104,"2013":105,"2014":102,"2015":105,"2016":106,"2017":102,"2018":107,"2019":106,"2020":108,"2021":109,"2022":110,"2023":111},4,2,5,8,9,13,10,20,22,15,{},{},{"meta":115,"data":117},{"total":116},"263",[118,230,328,430,522,638,706,798,879,958],{"id":119,"createTime":120,"updateTime":121,"relativeEntities":122,"slug":123,"properties":124,"entityType":131,"verifyStatus":132,"verifyTime":121,"verifyNote":133,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25,"primaryUrl":134,"fullTextUrl":24,"authors":135,"publicationType":190,"publisherRelationship":191,"citationCount":24,"citationInfo":24,"publishDate":227,"publishYear":228,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":24,"openAccess":24,"references":24,"isForceReanalyzing":229},"87742fc6-ee35-4dd0-b2e3-95e0f43d7859","2024-01-28T09:42:50.335+00:00","2025-02-22T23:58:00.824+00:00",[],"Channel-migration-and-its-impact-on-land-use-land-cover-using-RS-and-GIS-A-study-on-Khowai-River-of-Tripura-North-East-India",{"references":125,"title":127,"doi":129},{"VOID":126},"Ahmed, 2012, Detection of change in vegetation cover using multi-spectral and multi-temporal information for District Sargodha, Pakistan, Soc. Nat., 24, 557, 10.1590\u002FS1982-45132012000300014\nAyman, 2009, Meandering and bank erosion of the River Nile and its environmental impact on the area between Sohag and El-Minia, Egypt, Arab. J. Geosci., 4, 1\nBhakal, 2005, Estimation of bank erosion in the river Brahmaputra near Agyathuri by Using Geographic Information System, J. Ind. Soc. Remote Sens., 33, 81, 10.1007\u002FBF02989994\nBhowmik, M., Das (Pan) N., 2014. Qualitative Assessment of Bank Erosion Hazard in a Part of the Haora River, West Tripura District. In: M. Singh et al. (Eds.), Landscape Ecology and Water Management: Proceedings of IGU Rohtak Conference, 2.\nBoori, 2015, Land use\u002Fcover disturbance due to tourism in Jeseníky Mountain, Czech Republic: a remote sensing and GIS based approach, Egypt J. Remote Sens. Space Sci., 18, 17\nButt, 2015, Land use change mapping and analysis using Remote Sensing and GIS: a case study of Simly watershed, Islamabad, Pakistan, Egypt J. Remote Sens. Space Sci., 18, 251\nChakraborty, 2013, Causes and consequences of channel changes – a spatio-temporal analysis using remote sensing and GIS—Jaldhaka-Diana River System (Lower Course), Jalpaiguri (Duars), West Bengal, India, J. Geogr. Nat. Disasters, 3, 1\nChakraborty, 2015, An assessment on the nature of channel migration of River Diana of the sub-Himalayan West Bengal using field and GIS techniques, Arab. J. Geosci., 8, 5649, 10.1007\u002Fs12517-014-1594-5\nCongalton, 1999\nDas, 2013, Qualitative assessment of river bank erosion risk in Jirania rural development block, Tripura, Ind. J. Appl. Res., 3, 274, 10.15373\u002F2249555X\u002FJUNE2013\u002F91\nDas, 2014, Hydrodynamic changes of river course of part of Bhagirathi – Hugli in Nadia district – a Geoinformatics appraisal, Int. J. Geomatics Geosci., 5, 284\nDeb, 2012, Evaluation of meandering characteristics using RS & GIS of Manu River, J. Water Resour. Protec., 4, 163, 10.4236\u002Fjwarp.2012.43019\nGerard, 2010, Land cover change in Europe between 1950 and 2000 determined employing aerial photography, Prog. Phys. Geogr., 34, 183, 10.1177\u002F0309133309360141\nGogoi, 2013, A study on bank erosion and bank line migration pattern of the Subansiri River in Assam using remote sensing and GIS technology, Int. J. Eng. Sci., 2, 1\nGoswami, 2002, Channel pattern, sediment transport and bed regime of the Brahmaputra River, Assam, 143\nHazarika, 2015, Assessing land-use changes driven by river dynamics in chronically flood affected Upper Brahmaputra plains, India, using RS-GIS techniques, Egypt J. Remote Sens. Space Sci., 18, 107\nHickin, 1984, Lateral migration rates of river bends, J. Hydraul. Eng. Am. Soc. Civil. Eng., 110, 1557, 10.1061\u002F(ASCE)0733-9429(1984)110:11(1557)\nHoward, 1992, Modeling Channel Migration and Flood plain Sedimentation in Meandering Streams, 12\nIqbal, 2014, Spatiotemporal Land Use Land Cover change analysis and erosion risk mapping of Azad Jammu and Kashmir, Pakistan, Egypt. J. of Remote Sensing Space Sci., 17, 209, 10.1016\u002Fj.ejrs.2014.09.004\nJayanth, 2016, Identification of land cover changes in the coastal area of Dakshina Kannada district, South India during the year 2004–2008, Egypt. J. of Remote Sensing Space Sci., 19, 73, 10.1016\u002Fj.ejrs.2015.09.001\nJensen, J.R., 2005. Introductory Digital Image Processing: A Remote Sensing Perspective. 3rd Prentice Hall, Upper Saddle River, New Jersey.\nJensen, J.R., 2005. Digital change detection. Introductory Digital Image Processing, A Remote Sensing perspective. Pearson Prentice Hall, New York, pp. 467–494.\nKachhwala, 1985, Temporal monitoring of forest land for change detection and forest cover mapping through satellite remote sensing, 77\nKotoky, 2005, Nature of bank erosion along the Brahmaputra River channel, Assam, India. Cur. Sci., 88, 634\nKotoky, 2012, Changes in land use and land cover along the Dhansiri River Channel, Assam – a remote sensing and GIS approach, J. Geol. Soc. Ind., 79, 61, 10.1007\u002Fs12594-012-0002-6\nKummu, 2008, River bank Changes along the Mekong River: Remote Sensing Detection in the Vientiane-Nong Khai Area, Quater. Int., 186, 100, 10.1016\u002Fj.quaint.2007.10.015\nLandis, 1977, The measurement of observer agreement for categorical data, Biometrics, 33, 159, 10.2307\u002F2529310\nLu, 2004, Change detection techniques, Int. J. Remote Sens., 25, 2365, 10.1080\u002F0143116031000139863\nMajumdar, 2014, Spatio-temporal shift of right bank of the Gumti River, Amarpur Town, Tripura and its impact\nMosammam, 2016, Monitoring land use change and measuring urban sprawl based on its spatial forms, Egypt J. Remote Sens. Space Sci.\nMurthy, 1997, Temporal studies of land use\u002Fland cover in Varaha River Basin, Andhra Pradesh, India, J. Ind. Soc. Remote Sens., 25, 146, 10.1007\u002FBF03024215\nNanson, 1986, A statistical analysis of bank erosion and channel migration in Western Canada, Geol. Soc. Am. Bull., 97, 497, 10.1130\u002F0016-7606(1986)97\u003C497:ASAOBE>2.0.CO;2\nRahman, 2010, Impact of river bank erosion hazard in the Jamuna floodplain areas in Bangladesh, J. Sci. Found., 8, 55\nRawat, 2015, Monitoring land use\u002Fcover change using remote sensing and GIS techniques: a case study of Hawalbagh block, district Almora, Uttarakhand, India, Egypt J. Remote Sens. Space Sci., 18, 77\nRawat, 2013, Changes in land use\u002Fcover using geospatial techniques: a case study of Ramnagar town area, district Nainital, Uttarakhand, India, Egypt J. Remote Sens. Space Sci., 16, 111\nRogan, 2004, Remote sensing technology for mapping and monitoring land-cover and land-use change, Prog. Plan., 61, 301, 10.1016\u002FS0305-9006(03)00066-7\nSarma, 2012, A GIS based study on bank erosion by the river Brahmaputra around Kaziranga National Park, Assam, India, Earth Syst. Dyn. Discuss., 3, 1085, 10.5194\u002Fesdd-3-1085-2012\nSchumm, 1963, Sinuosity of alluvial rivers on the Great Plains, Bull. Geol. Soc. Am., 74, 1089, 10.1130\u002F0016-7606(1963)74[1089:SOAROT]2.0.CO;2\nSchumm, 1977, 337\nSun, 2009, Using Landsat data to determine land use changes in Datong basin, China, Environ. Geol., 57, 1825, 10.1007\u002Fs00254-008-1470-2\nSylla, 2012, A GIS technology and method to assess environmental problems from land use\u002Fcover changes: Conakry, Coyah and Dubreka region case study, Egypt J. Remote Sens. Space Sci., 15, 31\nThakur, 2012, River bank erosion hazard study of river Ganga, upstream of Farakka barrage using remote sensing and GIS, Nat. Hazards, 61, 967, 10.1007\u002Fs11069-011-9944-z\nWinterbottom, 2000, Medium and short-term channel planform changes on the rivers Tay and Tummel, Scotland, Geomorphology, 34, 195, 10.1016\u002FS0169-555X(00)00007-6\nYang, 1999, Satellite remote sensing and GIS for the analysis of channel migration changes in the active Yellow River Delta, China, Int. J. Appl. Earth Obs. Geoinf., 1, 146, 10.1016\u002FS0303-2434(99)85007-7\nYoudeowei, 1997, Bank collapse and erosion at the upper reaches of the Ekole creek in the Niger delta area of Nigeria, Bull. Int. Assoc. Eng. Geol., 55, 167, 10.1007\u002FBF02635419\nYuan, 2005, Land covers classification and change analysis of the Twin Cities (Minnesota), Remote Sens. Environ., 98, 317, 10.1016\u002Fj.rse.2005.08.006",{"EN":128},"Channel migration and its impact on land use\u002Fland cover using RS and GIS: A study on Khowai River of Tripura, North-East India",{"VOID":130},"10.1016\u002Fj.ejrs.2017.01.009","PUBLICATION","VERIFIED","Auto Verify","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1110982317300273",[136,153,166,178],{"id":137,"sortIndex":138,"researcher":24,"roles":139,"affiliations":141,"properties":150},"6a8d5d5f-30e3-4729-8c45-89019c8ae98c",3,[140],"AUTHOR",[142],{"id":24,"sortIndex":25,"affiliation":143,"properties":24},{"id":144,"createTime":145,"updateTime":145,"relativeEntities":146,"slug":24,"properties":147,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"4be8aedc-43b3-46d9-b1ae-7878e3152f7b","2024-01-28T09:42:50.370+00:00",[],{"title":148},{"VI":149},"Department of Geography and Disaster Management, Tripura University, Suryamaninagar 799022, India",{"title":151},{"VI":152},"Moujuri Bhowmik",{"id":154,"sortIndex":155,"researcher":24,"roles":156,"affiliations":157,"properties":163},"8421b437-2161-4c51-bcd1-b77f4a0bc7c0",1,[140],[158],{"id":24,"sortIndex":25,"affiliation":159,"properties":24},{"id":144,"createTime":145,"updateTime":145,"relativeEntities":160,"slug":24,"properties":161,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},[],{"title":162},{"VI":149},{"title":164},{"VI":165},"Nibedita Das (Pan)",{"id":167,"sortIndex":103,"researcher":24,"roles":168,"affiliations":169,"properties":175},"c3987dfd-4196-43af-bfb3-6fe855e0722c",[140],[170],{"id":24,"sortIndex":25,"affiliation":171,"properties":24},{"id":144,"createTime":145,"updateTime":145,"relativeEntities":172,"slug":24,"properties":173,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},[],{"title":174},{"VI":149},{"title":176},{"VI":177},"Istak Ahmed",{"id":179,"sortIndex":25,"researcher":24,"roles":180,"affiliations":181,"properties":187},"fc82f6a3-6b66-42ec-ba80-d5c6eaf7ec23",[140],[182],{"id":24,"sortIndex":25,"affiliation":183,"properties":24},{"id":144,"createTime":145,"updateTime":145,"relativeEntities":184,"slug":24,"properties":185,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},[],{"title":186},{"VI":149},{"title":188},{"VI":189},"Jatan Debnath","ARTICLE",{"url":134,"publisher":192,"properties":222},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":193,"slug":10,"properties":194,"entityType":22,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25,"subjectFields":200,"manageAffiliations":201,"indexDatabases":202,"url":97,"thumbnailPath":24,"statistic":217,"gsStatistic":24,"type":24,"analyzePriority":24},[],{"country":195,"issn":196,"introduce":197,"eissn":198,"title":199},{"VOID":13},{"VOID":15},{"EN":17},{"VOID":19},{"EN":21},[],[],[203,210],{"id":59,"indexDatabase":204,"url":72,"indexYears":73,"academicFieldIds":209,"indexDatabaseRanking":76},{"id":61,"createTime":62,"updateTime":63,"relativeEntities":205,"label":206,"description":207,"key":69,"publicationTags":208,"standard":24},[],{"EN":66,"VI":66},{"EN":66,"VI":68},[71],[75],{"id":78,"indexDatabase":211,"url":93,"indexYears":24,"academicFieldIds":216,"indexDatabaseRanking":24},{"id":80,"createTime":81,"updateTime":82,"relativeEntities":212,"label":213,"description":214,"key":89,"publicationTags":215,"standard":24},[],{"EN":85,"VI":85},{"VI":87,"EN":88},[91,92],[95,96],{"impactFactor":25,"impactFactorByYear":218,"i10Index":25,"i10IndexLast5Year":25,"totalPublication":100,"totalPublicationByYear":219,"totalCitation":25,"totalCitationByYear":220,"totalCitationPerPublication":25,"totalCitationPerPublicationByYear":221,"hindexLast5Year":25,"hindex":25},{},{"2010":102,"2011":103,"2012":104,"2013":105,"2014":102,"2015":105,"2016":106,"2017":102,"2018":107,"2019":106,"2020":108,"2021":109,"2022":110,"2023":111},{},{},{"volume":223,"pages":225},{"VOID":224},"20",{"VOID":226},"197-210","2017-12-01",2017,false,{"id":231,"createTime":232,"updateTime":233,"relativeEntities":234,"slug":235,"properties":236,"entityType":131,"verifyStatus":132,"verifyTime":233,"verifyNote":133,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25,"primaryUrl":243,"fullTextUrl":24,"authors":244,"publicationType":190,"publisherRelationship":290,"citationCount":24,"citationInfo":24,"publishDate":326,"publishYear":327,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":24,"openAccess":24,"references":24,"isForceReanalyzing":229},"2b06a3e9-7d33-414f-8bd4-a48ec9411e5b","2024-01-04T19:45:52.920+00:00","2024-12-19T23:55:48.364+00:00",[],"Assessment-of-potential-flash-flood-hazards-Concerning-land-use-land-cover-in-Aqaba-Governorate-Jordan-using-a-multi-criteria-technique",{"references":237,"title":239,"doi":241},{"VOID":238},"Abu El Shawashi, 2013, Using remote sensing techniques in evaluation of Wadi Yutum floods, Int. J. Environ. Water, 2, 4\nAbu El-Magd, 2020, Multi-criteria decision-making for the analysis of (FF): a case study of Awlad Toq-Sherq, Southeast Sohag, Egypt, J. Afr. Earth Sci., 162, 10.1016\u002Fj.jafrearsci.2019.103709\nAbu-Abdullah, 2020, A flood hazards management program of Wadi Baysh dam on the downstream area: an integration of hydrologic and hydraulic models, Jizan Region, KSA, Sustainability, 12, 1, 10.3390\u002Fsu12031069\nAbuqubu, J., Reyad A. Al Dwairi, R. Nafeth A. Hadi, B. Merkel, V., Dunger, Hamza A. Laila, 2016. Geological and Engineering Properties of Granite Rocks from Aqaba Area, South Jordan, Geomaterials, 6, 18-27.\nAl-Aqqad, 2015\nAlexander, 1991, Natural disasters: a framework for research and teaching, Disasters, 15, 209, 10.1111\u002Fj.1467-7717.1991.tb00455.x\nAl-husban, 2019, Urban expansion and shrinkage of vegetation cover in Al-Balqa Governorate, the Hashemite Kingdom of Jordan, Environ. Earth Sci., 78, 620, 10.1007\u002Fs12665-019-8635-z\nAllafta, 2021, GIS-based multi-criteria analysis for flood prone areas mapping in the trans-boundary Shatt Al-Arab basin, Iraq-Iran, Geomat. Nat. Haz. Risk, 12, 2087, 10.1080\u002F19475705.2021.1955755\nAL-Taani, 2021, Land suitability evaluation for agricultural use using GIS and remote sensing techniques: The case study of Ma’an Governorate, Jordan, The Egyptian Journal of Remote Sensing and Space, Sciences,24,, 109, 10.1016\u002Fj.ejrs.2020.01.001\nAl-Weshah, 1999, Flood Analysis and Mitigation for Petra Area in Jordan, J. Water Resour. Plan. Manag., 125, 170, 10.1061\u002F(ASCE)0733-9496(1999)125:3(170)\nBathrellos, 2016, Urban flood hazard assessment in the basin of Athens Metropolitan city, Greece, Environ. Earth Sci., 75, 319, 10.1007\u002Fs12665-015-5157-1\nBurdon, 1959, Handbook of the Geology of Jordan. To Accompany and Explain the Three Sheets of the 1: 250 ,000 Geological Map of Jordan East of the Rift by A.M. Quennel, Government of the Hashemite Kingdom of Jordan, 82\nCongalton, 1991, A review of assessing the accuracy of remotely sensed data, Rem. Sens. Environ., 37, 35, 10.1016\u002F0034-4257(91)90048-B\nCongalton, 2008\nCzigány, S.; Pirkhoffer, E. & Geresdi, I. 2008, Environmental impacts of flash floods inHungary, In: Flood Risk Management: Research and Practice, Samuels, P., Huntin gton,S., Allsop, W. & Harrop, J., (Eds.), pp. 1439–1447, Taylor & Francis Group, ISBN978-0-415-48507-4, London, UK.\nFarhan, 2016, Flash flood hazards estimation of Wadi Yutum (Southern Jordan) watershed using GIS based morphometric analysis and remote sensing techniques, Open J. Modern Hydrol., 6, 79, 10.4236\u002Fojmh.2016.62008\nGaˇnová, L.; Zele ˇnáková, M.; Purcz, P.; Kuzeviˇcová, Ž.; Hlavatá, H., 2013.A rainfall distribution and their influence on flood generation in the eastern Slovakia. Acta Univ. Agric. Silvicult. Mendel. Brun., 61, 1645–1652.\nHorton, 1945, Erosional development of streams and their drainage basins: hydrophysical approach to quantitative morphology, Geol. Soc. Am. Bull., 56, 275, 10.1130\u002F0016-7606(1945)56[275:EDOSAT]2.0.CO;2\nHuali, 2015, Flood hazard assessment in the Kujukuri Plain of Chiba Prefecture, Japan, based on GIS and multicriteria decision analysis, Nat. Hazards, 78, 105, 10.1007\u002Fs11069-015-1699-5\nI KEYA, H., 2008: A Proposal on Arid Land Sabo Works - a case study in Jordan: http:\u002F\u002Fwww.sabo-int.org\u002Fprojects\u002Fjordan.pdf.\nIAHS-UNESCO-WMO (1974) (FF). Proceedings of the Paris Symposium,Publication no. 112.https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00254-007-0969-2 [4] NSW\u002FNOAA.\nIsioye, O.A., Shebe, M.W. Momoh, UI.,. and Bako, C.N. 2012. A MultiCriteria Decision Support System (MDSS) for identifying rain water harvesting site (s) in Zaria, Kaduna State, Nigeria. International Journal of Advanced Scientific Engineering and Technological Research, 1, 53-71.\nKazama, S.; Sato, A.; Kawagoe, S. 2009.Evaluating the Cost of Flood Damage Based on Changes in Extreme Rainfall in Japan. Sustain. Sci., 4, 61–69.\nKhresat, 1998, Properties and characterization of vertisols developed on limestone in a semi-arid environment, J. Arid Environ., 40, 235, 10.1006\u002Fjare.1998.0445\nMeigs, 1953, World distribution of arid and semi-arid homoclimates\nMousavi, S.M., Roostaei, S., and Rostamzadeh, H., 2019. Estimation of flood land use\u002Fland cover mapping by regional modelling of flood hazard at sub-basin level case study: Marand basin.,Gemomatics, Natural Hazards and Hazards 2019, 10, no. 1, 1155–1175.\nPirnazar, M. Karimi, A.Z. Bakhtiar Feizizadeh Kaveh Ostad-Ali-Askari, Saeid Eslamian, Hafez asheminasab,2017, Assessing Flood Hazard Using GIS-based Multicriteria Decision-making Approach, Study Area: East-Azerbaijan Province (Kaleybar Chay Basin), Journal of Flood Engineering 8(2), 203-223.\nRahmati, 2016, Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis, Geomat. Nat. Hazards Hazards, 7, 1000, 10.1080\u002F19475705.2015.1045043\nSatty, 2008, Decision making with the analytic hierarchy process, Int. J. Serv. Sci., 1, 83\nSoha A. M. and E. El‑Raey E.M., 2020.Vulnerability assessment for (FF) using GIS spatial modeling and remotely sensed data in El‑Arish City, North Sinai, Egypt, Natural Hazards, 102,pages707–728 https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11069-019-03571-x.\nSreedevi, 2013, Drainage morphometry and its influence on hydrology in a semi arid region: using SRTM data and GIS, Environ. Earth Sci., 70, 839, 10.1007\u002Fs12665-012-2172-3\nStrahler, A. 1964.Quantitative Geomorphology of Drainage Basin and Channel Networks. In: Chow, V.T., Ed., Handbook of Applied Hydrology. McGraw Hill,New York. Amboy, New Jersey. Geological Society of America Bulletin, 67, 597-646. https:\u002F\u002Fdoi.org\u002F10.1130\u002F0016-7606(1956)67[597:EODSAS]2.0.CO;2.\nVojtek, 2019, 2019, Flood Susceptibility Mapping on a National Scale in Slovakia Using the Analytical Hierarchy Process, Water, 11, 364, 10.3390\u002Fw11020364\nWorld Food Programme (WFP) (2019) Flood Hazard Map Integrated Context Analysis Jordan July 2019.\nXiao, L. 1999.(FF) in Arid and Semi Arid Zones. International HydrologicalProgram. Technical Documents in Hydrology No. 23 UNESCO, Paris.\nYoussef, 2021, The devastating flood in the arid region a consequence of rainfall and dam failure: Case study, Al-Lith flood on 23th November 2018, Kingdom of Saudi Arabia, Z. Geomorphol., 63, 115, 10.1127\u002Fzfg\u002F2021\u002F0672",{"EN":240},"Assessment of potential flash flood hazards. Concerning land use\u002Fland cover in Aqaba Governorate, Jordan, using a multi-criteria technique",{"VOID":242},"10.1016\u002Fj.ejrs.2022.12.007","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1110982322001193",[245,260,275],{"id":246,"sortIndex":155,"researcher":24,"roles":247,"affiliations":248,"properties":257},"6a3f321e-0639-45f1-b431-53f717cd8f2e",[140],[249],{"id":24,"sortIndex":25,"affiliation":250,"properties":24},{"id":251,"createTime":252,"updateTime":252,"relativeEntities":253,"slug":24,"properties":254,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"19c669a0-7ead-49b8-9e02-4c534dca184b","2024-01-04T19:45:52.953+00:00",[],{"title":255},{"VI":256},"Department of Geography, Faculty of Arts, The University of Jordan, Jordan",{"title":258},{"VI":259},"Yusra Al-husban",{"id":261,"sortIndex":25,"researcher":24,"roles":262,"affiliations":263,"properties":272},"cbac071c-02a7-47ba-9711-c867b8925993",[140],[264],{"id":24,"sortIndex":25,"affiliation":265,"properties":24},{"id":266,"createTime":267,"updateTime":267,"relativeEntities":268,"slug":24,"properties":269,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"24a313e3-834d-4962-88c8-ea0f1eaa2ac7","2024-01-03T06:15:31.685+00:00",[],{"title":270},{"VI":271},"Department of Applied Geography, Faculty of Arts and Humanities, The University of AL al – Bayt, Jordan",{"title":273},{"VI":274},"Aymen Al-Taani",{"id":276,"sortIndex":103,"researcher":24,"roles":277,"affiliations":278,"properties":287},"bdb8ed8f-ff36-4b0c-982c-400cf3404206",[140],[279],{"id":24,"sortIndex":25,"affiliation":280,"properties":24},{"id":281,"createTime":282,"updateTime":282,"relativeEntities":283,"slug":24,"properties":284,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"4cba45c6-5814-439a-82ba-2ef6e25fa98d","2024-01-04T19:45:52.971+00:00",[],{"title":285},{"VI":286},"Queen Rania Center for Education and Information Technology, Amman 11814, Jordan",{"title":288},{"VI":289},"Ahmad Ayan",{"url":243,"publisher":291,"properties":321},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":292,"slug":10,"properties":293,"entityType":22,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25,"subjectFields":299,"manageAffiliations":300,"indexDatabases":301,"url":97,"thumbnailPath":24,"statistic":316,"gsStatistic":24,"type":24,"analyzePriority":24},[],{"country":294,"issn":295,"introduce":296,"eissn":297,"title":298},{"VOID":13},{"VOID":15},{"EN":17},{"VOID":19},{"EN":21},[],[],[302,309],{"id":59,"indexDatabase":303,"url":72,"indexYears":73,"academicFieldIds":308,"indexDatabaseRanking":76},{"id":61,"createTime":62,"updateTime":63,"relativeEntities":304,"label":305,"description":306,"key":69,"publicationTags":307,"standard":24},[],{"EN":66,"VI":66},{"EN":66,"VI":68},[71],[75],{"id":78,"indexDatabase":310,"url":93,"indexYears":24,"academicFieldIds":315,"indexDatabaseRanking":24},{"id":80,"createTime":81,"updateTime":82,"relativeEntities":311,"label":312,"description":313,"key":89,"publicationTags":314,"standard":24},[],{"EN":85,"VI":85},{"VI":87,"EN":88},[91,92],[95,96],{"impactFactor":25,"impactFactorByYear":317,"i10Index":25,"i10IndexLast5Year":25,"totalPublication":100,"totalPublicationByYear":318,"totalCitation":25,"totalCitationByYear":319,"totalCitationPerPublication":25,"totalCitationPerPublicationByYear":320,"hindexLast5Year":25,"hindex":25},{},{"2010":102,"2011":103,"2012":104,"2013":105,"2014":102,"2015":105,"2016":106,"2017":102,"2018":107,"2019":106,"2020":108,"2021":109,"2022":110,"2023":111},{},{},{"volume":322,"pages":324},{"VOID":323},"26",{"VOID":325},"17-24","2023-02-01",2023,{"id":329,"createTime":330,"updateTime":331,"relativeEntities":332,"slug":333,"properties":334,"entityType":131,"verifyStatus":132,"verifyTime":331,"verifyNote":133,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25,"primaryUrl":341,"fullTextUrl":24,"authors":342,"publicationType":190,"publisherRelationship":392,"citationCount":24,"citationInfo":24,"publishDate":428,"publishYear":429,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":24,"openAccess":24,"references":24,"isForceReanalyzing":229},"aeca2eb4-de10-496c-96ba-c74f022b6224","2023-12-23T02:39:49.475+00:00","2025-01-06T23:55:11.838+00:00",[],"Implementation-of-a-topographic-artificial-neural-network-wind-speed-prediction-model-for-assessing-onshore-wind-power-potential-in-Sibu-Sarawak",{"references":335,"title":337,"doi":339},{"VOID":336},"S.A.P. Kani, S. Member, G.H. Riahy, “A New ANN-Based Methodology for Very Short-Term Wind Speed Prediction Using Markov Chain Approach,” pp. 1–6, 2012.\nUmmels, 2009, Comparison of integration solutions for wind power in the Netherlands, IET Renew. Power Gener., 3, 279, 10.1049\u002Fiet-rpg.2008.0080\nK. Chooi Tan, H. San Lim, M. Zubir Mat Jafri, “Study on solar ultraviolet erythemal dose distribution over Peninsular Malaysia using Ozone Monitoring Instrument,” 2017.\nJashnani, 2013, Sizing and preliminary hardware testing of solar powered UAV, Egypt. J. Remote Sens. Sp. Sci., 16, 189\nS.M. Lawan, W.A.W.Z. Abidin, W.Y. Chai, A. Baharun, T. Masri, “(WRA) Techniques, wind energy potential and utilisation in malaysia and other countries,” vol. 8, no. 12, pp. 1039–1053, 2013.\nCelik, 2013, Generalized feed-forward based method for wind energy prediction, Appl. Energy, 101, 582, 10.1016\u002Fj.apenergy.2012.06.040\nPourmousavi Kani, 2011, Very short-term wind speed prediction: a new artificial neural network–Markov chain model, Energy Convers. Manag., 52, 738, 10.1016\u002Fj.enconman.2010.07.053\nDaut, 2012, A Study on the wind as renewable energy in Perlis, Northern Malaysia, Energy Procedia, 18, 1428, 10.1016\u002Fj.egypro.2012.05.159\nY. Himri, S. Himri, A.B. Stambouli, “Wind Speed Data Analysis used in Installation of Wind Energy Conversion Systems in Algeria,” no. 8000, pp. 1–5, 2010.\nAhmed, 2012, A Statistical Analysis of Wind Power Density Based on the Weibull and Ralyeigh models of ‘ Penjwen Region ’ Sulaimani \u002F Iraq, JJMIE, 6, 135\nCarta, 2009, A review of wind speed probability distributions used in wind energy analysis, Renew. Sustain. Energy Rev., 13, 933, 10.1016\u002Fj.rser.2008.05.005\nF.L. Ludwig, “Mass-consistent flow fields from wind observations in rough terrain + I,” no. 3, 1980.\nN. Brahmi, S. Sallem, M. Chaabene, “ANN based parameters estimation of Weibull: Application to wind energy potential assessment of Sfax, Tunisia,” irec.cmerp.net, no. 3, pp. 203–207, 2010.\nM. Monfared, S.K.Y. Nikravesh, H. Rastegar, “A Novel Fuzzy Predictor for Wind Speed,” pp. 840–843, 2007.\nR. Ata, “Artificial neural networks applications in wind energy systems: a review,” 2015.\nSoman, 2010, A review of wind power and wind speed forecasting methods with different time horizons, North Am. Power",{"EN":338},"Implementation of a topographic artificial neural network wind speed prediction model for assessing onshore wind power potential in Sibu, Sarawak",{"VOID":340},"10.1016\u002Fj.ejrs.2019.08.003","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1110982317304015",[343,358,380],{"id":344,"sortIndex":155,"researcher":24,"roles":345,"affiliations":346,"properties":355},"5d2d83c9-c1b8-4cba-9389-f21609abdc76",[140],[347],{"id":24,"sortIndex":25,"affiliation":348,"properties":24},{"id":349,"createTime":350,"updateTime":350,"relativeEntities":351,"slug":24,"properties":352,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"b3571fd2-b460-463f-a86d-ba7a456fa638","2023-12-23T02:39:49.631+00:00",[],{"title":353},{"VI":354},"Department of Electrical and Electronics Engineering, Faculty of Engineering, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia",{"title":356},{"VI":357},"W.A.W.Z. Abidin",{"id":359,"sortIndex":25,"researcher":24,"roles":360,"affiliations":361,"properties":377},"a776413d-8c8f-486f-b169-ce9e862c8dce",[140],[362,369],{"id":363,"sortIndex":155,"affiliation":364,"properties":368},"b596a13f-2730-4f6c-a67d-30b29ed3fa2c",{"id":349,"createTime":350,"updateTime":350,"relativeEntities":365,"slug":24,"properties":366,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},[],{"title":367},{"VI":354},{},{"id":24,"sortIndex":25,"affiliation":370,"properties":24},{"id":371,"createTime":372,"updateTime":372,"relativeEntities":373,"slug":24,"properties":374,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"6f3c5e19-e70d-490e-94c9-2f3ec9092bd6","2023-12-23T02:39:49.488+00:00",[],{"title":375},{"VI":376},"Department of Electrical Engineering, Kano University of Science and Technology, PMB 3244, Kano State, Nigeria",{"title":378},{"VI":379},"S.M. Lawan",{"id":381,"sortIndex":103,"researcher":24,"roles":382,"affiliations":383,"properties":389},"7b85136c-1e22-4776-b7d6-e0bf8f1e555e",[140],[384],{"id":24,"sortIndex":25,"affiliation":385,"properties":24},{"id":349,"createTime":350,"updateTime":350,"relativeEntities":386,"slug":24,"properties":387,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},[],{"title":388},{"VI":354},{"title":390},{"VI":391},"T. Masri",{"url":341,"publisher":393,"properties":423},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":394,"slug":10,"properties":395,"entityType":22,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25,"subjectFields":401,"manageAffiliations":402,"indexDatabases":403,"url":97,"thumbnailPath":24,"statistic":418,"gsStatistic":24,"type":24,"analyzePriority":24},[],{"country":396,"issn":397,"introduce":398,"eissn":399,"title":400},{"VOID":13},{"VOID":15},{"EN":17},{"VOID":19},{"EN":21},[],[],[404,411],{"id":59,"indexDatabase":405,"url":72,"indexYears":73,"academicFieldIds":410,"indexDatabaseRanking":76},{"id":61,"createTime":62,"updateTime":63,"relativeEntities":406,"label":407,"description":408,"key":69,"publicationTags":409,"standard":24},[],{"EN":66,"VI":66},{"EN":66,"VI":68},[71],[75],{"id":78,"indexDatabase":412,"url":93,"indexYears":24,"academicFieldIds":417,"indexDatabaseRanking":24},{"id":80,"createTime":81,"updateTime":82,"relativeEntities":413,"label":414,"description":415,"key":89,"publicationTags":416,"standard":24},[],{"EN":85,"VI":85},{"VI":87,"EN":88},[91,92],[95,96],{"impactFactor":25,"impactFactorByYear":419,"i10Index":25,"i10IndexLast5Year":25,"totalPublication":100,"totalPublicationByYear":420,"totalCitation":25,"totalCitationByYear":421,"totalCitationPerPublication":25,"totalCitationPerPublicationByYear":422,"hindexLast5Year":25,"hindex":25},{},{"2010":102,"2011":103,"2012":104,"2013":105,"2014":102,"2015":105,"2016":106,"2017":102,"2018":107,"2019":106,"2020":108,"2021":109,"2022":110,"2023":111},{},{},{"volume":424,"pages":426},{"VOID":425},"23",{"VOID":427},"21-34","2020-04-01",2020,{"id":431,"createTime":432,"updateTime":433,"relativeEntities":434,"slug":435,"properties":436,"entityType":131,"verifyStatus":132,"verifyTime":433,"verifyNote":133,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25,"primaryUrl":443,"fullTextUrl":24,"authors":444,"publicationType":190,"publisherRelationship":484,"citationCount":24,"citationInfo":24,"publishDate":520,"publishYear":521,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":24,"openAccess":24,"references":24,"isForceReanalyzing":229},"1f70a86a-562d-4ec3-af53-9fea5ab4879b","2023-12-07T20:10:00.777+00:00","2025-02-16T23:47:23.547+00:00",[],"Characterization-of-landscape-features-associated-with-mosquito-breeding-in-urban-Cairo-using-remote-sensing",{"references":437,"title":439,"doi":441},{"VOID":438},"Abdel-Megeed, 2003, Toxicity of certain insecticides against mosquito larvae Culex pipiens (Diptera: Culicidae), J. Environ. Sci., 6, 575\nAmmar, 2012, Ecology of the mosquito larvae in urban environments of Cairo Governorate, Egypt. J. Egypt Soc. Parasitol., 42, 191, 10.12816\u002F0006307\nBeck, 2000, Remote sensing and human health: new sensors and new opportunities, Emerg. Infect. Dis., 6, 217, 10.3201\u002Feid0603.000301\nBogh, 2007, High spatial resolution mapping of malaria transmission risk in the Gambia, west Africa, using Landsat TM satellite imagery, Am. J. Trop. Med. Hyg., 76, 875, 10.4269\u002Fajtmh.2007.76.875\nCAPMAS, 2006. Central agency for public mobilization and statistics. CAPMAS web site: \u003Chttp:\u002F\u002Fwww.msrintranet.capmas.gov.eg>.\nEgyptian Meteorological Authority, 1996. The Climatic Atlas of Egypt. Cairo.\nEisele, 2003, Linking field-based ecological data with remotely sensed data using a geographic information system in two malaria endemic urban areas of Kenya, Malaria J., 2, 44, 10.1186\u002F1475-2875-2-44\nEl Shewiy, M., 1996. Environmental problems related to groundwater conditions within Greater Cairo, Institute of environment studies and research. Unpublished thesis, Ain Shams University, Cairo, Egypt.\nGamba, 2003, Foreword to the special issue on urban remote sensing by satellite, IEEE Trans. Geosci. Remote Sensing, 41, 1903, 10.1109\u002FTGRS.2003.816572\nGlick, 1992, Illustrated key to the female anopheles of Southwestern Asia and Egypt (Diptera: Culicidae), Mosq. Syst., 24, 125\nHarbach, 1985, Pictorial keys to the genera of mosquitoes, subgenera of Culex and the species of Culex (Culex) occurring in Southwestern Asia and Egypt, with a note on the subgeneric placement of Culex deserticola (Diptera: Culicidae), Mosq. Syst., 17, 83\nHarbach, 1988, Mosquitoes of the subgenus Culex in Southwestern Asia and Egypt (Diptera: Culicidae), Contr. Am. Entomol. Inst. (Ann Arbor), 24, 1\nImpoinvil, 2008, The role of unused swimming pools as a habitat for Anopheles immature stages in urban Malindi, Kenya J. Am. Mosq. Control Assoc., 24, 457, 10.2987\u002F5739.1\nJacob, 2006, Spatially targeting Culex quinquefasciatus aquatic habitats on modified land cover for implementing an integrated vector management (IVM) program in three villages within the Mwea Rice Scheme, Kenya Int. J. Health Geogr., 9, 5\nJensen, 1999, Remote sensing of urban suburban infrastructure and socio-economic attributes, Photogrammetric Eng. Remote Sensing, 65, 611\nKlinkenberg, 2008, Impact of urban agriculture on malaria vectors in Accra, Ghana Malaria J., 7, 151, 10.1186\u002F1475-2875-7-151\nKnudsen, 1992, Vector-borne disease problems in rapid urbanization: new approaches to vector control, Bull. World Health Organ., 70, 1\nMasuoka, 2003, Use of Ikonos and Landsat for malaria control in the Republic of Korea, Remote Sensing Environ., 88, 187, 10.1016\u002Fj.rse.2003.04.009\nMorsy, 2004, Seasonal distribution of Culicini larvae in Greater Cairo, J. Egypt Soc. Parasitol., 34, 143\nMushinzimana, 2006, Landscape determinants and remote sensing of anopheline mosquito larval habitats in the Western Kenya highlands, Malaria J., 16, 5\nO’Malley, 1995, Seven ways to a successful dipping career, Wing Beats, 6, 23\nOssman, A.S., 2001. Policies of Directing Cairo Urban Expansion. Ph.D. Unpublished thesis, Ain Shams University, Faculty of Engineering, Department of Urban Planning.\nRongnoparut, 2005, Use of a remote sensing-based geographic information system in the characterizing spatial patterns for Anopheles minimus A and C breeding habitats in Western Thailand, Southeast Asian J. Trop. Med. Public Health, 36, 1145\nSchmidt-Vogt, 1999, vol. 8\nSeverini, 2008, Aedes albopictus in Rome: results and perspectives after 10years of monitoring, Parassitologia, 50, 121\nSmall, 2003, High spatial resolution spectral mixture analysis of urban reflectance, Remote Sensing Environ., 88, 170, 10.1016\u002Fj.rse.2003.04.008\nStoops, 2008, Remotely-sensed land use patterns and the presence of anopheles larvae (Diptera: Culicidae) in Sukabumi, West Java, Indones. J. Vector Ecol., 33, 30, 10.3376\u002F1081-1710(2008)33[30:RLUPAT]2.0.CO;2\nTucker, 1979, Red and photographic infrared linear combinations for monitoring vegetation, Remote Sensing Environ., 8, 127, 10.1016\u002F0034-4257(79)90013-0",{"EN":440},"Characterization of landscape features associated with mosquito breeding in urban Cairo using remote sensing",{"VOID":442},"10.1016\u002Fj.ejrs.2012.12.002","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1110982312000488",[445,460,472],{"id":446,"sortIndex":103,"researcher":24,"roles":447,"affiliations":448,"properties":457},"8fae7388-84cd-46d8-b473-244550714000",[140],[449],{"id":24,"sortIndex":25,"affiliation":450,"properties":24},{"id":451,"createTime":452,"updateTime":452,"relativeEntities":453,"slug":24,"properties":454,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"95b61fd6-1447-45e4-affb-1e2accb28e74","2023-12-07T20:10:00.802+00:00",[],{"title":455},{"VI":456},"Institute of Environmental Studies & Research, Ain Shams University, Cairo, Egypt",{"title":458},{"VI":459},"Hala A. Kassem",{"id":461,"sortIndex":25,"researcher":24,"roles":462,"affiliations":463,"properties":469},"d9a2dd39-4f93-46ef-be83-9fe9289ff386",[140],[464],{"id":24,"sortIndex":25,"affiliation":465,"properties":24},{"id":451,"createTime":452,"updateTime":452,"relativeEntities":466,"slug":24,"properties":467,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},[],{"title":468},{"VI":456},{"title":470},{"VI":471},"Ali N. Hassan",{"id":473,"sortIndex":155,"researcher":24,"roles":474,"affiliations":475,"properties":481},"a945b435-24aa-4a7e-8ced-5b8b52886b18",[140],[476],{"id":24,"sortIndex":25,"affiliation":477,"properties":24},{"id":451,"createTime":452,"updateTime":452,"relativeEntities":478,"slug":24,"properties":479,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},[],{"title":480},{"VI":456},{"title":482},{"VI":483},"Nihad El Nogoumy",{"url":443,"publisher":485,"properties":515},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":486,"slug":10,"properties":487,"entityType":22,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25,"subjectFields":493,"manageAffiliations":494,"indexDatabases":495,"url":97,"thumbnailPath":24,"statistic":510,"gsStatistic":24,"type":24,"analyzePriority":24},[],{"country":488,"issn":489,"introduce":490,"eissn":491,"title":492},{"VOID":13},{"VOID":15},{"EN":17},{"VOID":19},{"EN":21},[],[],[496,503],{"id":59,"indexDatabase":497,"url":72,"indexYears":73,"academicFieldIds":502,"indexDatabaseRanking":76},{"id":61,"createTime":62,"updateTime":63,"relativeEntities":498,"label":499,"description":500,"key":69,"publicationTags":501,"standard":24},[],{"EN":66,"VI":66},{"EN":66,"VI":68},[71],[75],{"id":78,"indexDatabase":504,"url":93,"indexYears":24,"academicFieldIds":509,"indexDatabaseRanking":24},{"id":80,"createTime":81,"updateTime":82,"relativeEntities":505,"label":506,"description":507,"key":89,"publicationTags":508,"standard":24},[],{"EN":85,"VI":85},{"VI":87,"EN":88},[91,92],[95,96],{"impactFactor":25,"impactFactorByYear":511,"i10Index":25,"i10IndexLast5Year":25,"totalPublication":100,"totalPublicationByYear":512,"totalCitation":25,"totalCitationByYear":513,"totalCitationPerPublication":25,"totalCitationPerPublicationByYear":514,"hindexLast5Year":25,"hindex":25},{},{"2010":102,"2011":103,"2012":104,"2013":105,"2014":102,"2015":105,"2016":106,"2017":102,"2018":107,"2019":106,"2020":108,"2021":109,"2022":110,"2023":111},{},{},{"volume":516,"pages":518},{"VOID":517},"16",{"VOID":519},"63-69","2013-06-01",2013,{"id":523,"createTime":524,"updateTime":525,"relativeEntities":526,"slug":527,"properties":528,"entityType":131,"verifyStatus":132,"verifyTime":525,"verifyNote":133,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25,"primaryUrl":535,"fullTextUrl":24,"authors":536,"publicationType":190,"publisherRelationship":603,"citationCount":24,"citationInfo":24,"publishDate":326,"publishYear":327,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":24,"openAccess":24,"references":24,"isForceReanalyzing":229},"32c911fe-eade-4e5a-916d-40c04ce70a84","2024-01-12T10:07:03.856+00:00","2024-12-28T23:46:14.714+00:00",[],"An-improved-generative-adversarial-networks-for-remote-sensing-image-super-resolution-reconstruction-via-multi-scale-residual-block",{"references":529,"title":531,"doi":533},{"VOID":530},"Ahn, N., Kang, B., Sohn, K.A., 2018. Fast, accurate, and lightweight super-resolution with cascading residual network, in: Proceedings of the European conference on computer vision (ECCV), pp. 252–268.\nBerger, 1994, An overview of robust bayesian analysis, Test, 3, 5, 10.1007\u002FBF02562676\nChen, 2022, Feature fusion and kernel selective in inception-v4 network, Applied Soft Computing, 119, 10.1016\u002Fj.asoc.2022.108582\nChen, H., Wang, Y., Xu, C., et al., 2020. Addernet: Do we really need multiplications in deep learning?, in: Proceedings of the IEEE\u002FCVF conference on computer vision and pattern recognition, pp. 1468–1477.\nDiniz, 1997, volume 4\nDong, 2015, Image super-resolution using deep convolutional networks, IEEE transactions on pattern analysis and machine intelligence, 38, 295, 10.1109\u002FTPAMI.2015.2439281\nEs-SAFI, 2016, HARCHLI: Adaptation of multilayer perceptron neural network to unsupervised clustering using a developed version of k-means algorithm, WSEAS Transactions on Computers, 15, 103\nEuijeong, 2021, Srps–deep-learning-based photometric stereo using superresolution images, Journal of Computational Design and Engineering, 4\nGao, 2019, Res2net: A new multi-scale backbone architecture, IEEE transactions on pattern analysis and machine intelligence, 43, 652, 10.1109\u002FTPAMI.2019.2938758\nGlasner, D., Bagon, S., Irani, M., 2009. Super-resolution from a single image, in: 2009 IEEE 12th international conference on computer vision, IEEE. pp. 349–356.\nGomes, 2019, Machine learning for streaming data: state of the art, challenges, and opportunities, ACM SIGKDD Explorations Newsletter, 21, 6, 10.1145\u002F3373464.3373470\nGuo, R., Shi, X.P., Jia, D.K., 2018. Learning a deep convolutional network for image super-resolution reconstruction. Journal of Engineering of Heilongjiang University.\nHou, 2020, A novel and effective image super-resolution reconstruction technique via fast global and local residual learning model, Applied Sciences, 10, 1856, 10.3390\u002Fapp10051856\nHuang, G., Liu, S., Van der Maaten, L., et al., 2018. Condensenet: An efficient densenet using learned group convolutions, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2752–2761.\nKim, J., Lee, J.K., Lee, K.M., 2016a. Accurate image super-resolution using very deep convolutional networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1646–1654.\nKim, J., Lee, J.K., Lee, K.M., 2016b. Deeply-recursive convolutional network for image super-resolution, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1637–1645.\nLedig, C., Theis, L., Huszár, F., et al., 2017. Photo-realistic single image super-resolution using a generative adversarial network, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4681–4690.\nLee, 2020, Learning with privileged information for efficient image super-resolution, 465\nLi, 2020, Learning a deep dual attention network for video super-resolution, IEEE transactions on image processing, 29, 4474, 10.1109\u002FTIP.2020.2972118\nLi, J., Fang, F., Mei, K., et al., 2018. Multi-scale residual network for image super-resolution, in: Proceedings of the European conference on computer vision (ECCV), pp. 517–532.\nLi, 2020, Sacnn: Self-attention convolutional neural network for low-dose ct denoising with self-supervised perceptual loss network, IEEE transactions on medical imaging, 39, 2289, 10.1109\u002FTMI.2020.2968472\nLiu, 2020, Lightweight multi-scale residual networks with attention for image super-resolution, Knowledge-Based Systems, 203, 10.1016\u002Fj.knosys.2020.106103\nLiu, 2019, An efficient residual learning neural network for hyperspectral image superresolution, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 1240, 10.1109\u002FJSTARS.2019.2901752\nMa, C., Rao, Y., Cheng, Y., et al., 2020. Structure-preserving super resolution with gradient guidance, in: Proceedings of the IEEE\u002FCVF conference on computer vision and pattern recognition, pp. 7769–7778.\nNedić, A., 2010. Random projection algorithms for convex set intersection problems, in: 49th IEEE Conference on Decision and Control (CDC), IEEE. pp. 7655–7660.\nOlshausen, 2004, Sparse coding of sensory inputs, Current opinion in neurobiology, 14, 481, 10.1016\u002Fj.conb.2004.07.007\nOuyang, 2019, Ultra-low-dose pet reconstruction using generative adversarial network with feature matching and task-specific perceptual loss, Medical physics, 46, 3555, 10.1002\u002Fmp.13626\nPeng, X., Yongping, L.I., Zhang, X., 2016. Binocular stereo matching algorithm based on deep learning.\nQin, 2020, Multi-scale feature fusion residual network for single image super-resolution, Neurocomputing, 379, 334, 10.1016\u002Fj.neucom.2019.10.076\nRojo-Álvarez, 2007, Nonuniform interpolation of noisy signals using support vector machines, IEEE Transactions on Signal Processing, 55, 4116, 10.1109\u002FTSP.2007.896029\nShi, 2018, Super-resolution reconstruction of mr image with a novel residual learning network algorithm, Physics in Medicine & Biology, 63, 10.1088\u002F1361-6560\u002Faab9e9\nShi, W., Caballero, J., Huszár, F., et al., 2016. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1874–1883.\nWang, X., Yu, K., Wu, S., et al., 2018. Esrgan: Enhanced super-resolution generative adversarial networks, in: Proceedings of the European conference on computer vision (ECCV) workshops, pp. 0–0.\nWang, Y., Ying, X., 2021. Symmetric parallax attention for stereo image super-resolution, in: Computer Vision and Pattern Recognition.\nWoo, S., Park, J., Lee, J.Y., et al., 2018. Cbam: Convolutional block attention module, in: Proceedings of the European conference on computer vision (ECCV), pp. 3–19.\nWunsch, P., Hirzinger, G., 1996. Registration of cad-models to images by iterative inverse perspective matching, in: Proceedings of 13th International Conference on Pattern Recognition, IEEE. pp. 78–83.\nYang, 2018, Low-dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss, IEEE transactions on medical imaging, 37, 1348, 10.1109\u002FTMI.2018.2827462\nZamir, 2020, Learning enriched features for real image restoration and enhancement, 492\nZeng, Y., Fu, J., Chao, H., et al., 2019. Learning pyramid-context encoder network for high-quality image inpainting, pp. 1486–1494.\nZhang, T., Gu, Y., Huang, X., 2020. Stereo endoscopic image super-resolution using disparity-constrained parallel attention.\nZhang, Y., Li, K., Li, K., et al., 2018. Image super-resolution using very deep residual channel attention networks, in: Proceedings of the European conference on computer vision (ECCV), pp. 286–301.\nZhou, 2016, Image super-resolution via sparse representation, Computer Engineering and Design",{"EN":532},"An improved generative adversarial networks for remote sensing image super-resolution reconstruction via multi-scale residual block",{"VOID":534},"10.1016\u002Fj.ejrs.2022.12.008","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS111098232200120X",[537,552,567,579,591],{"id":538,"sortIndex":103,"researcher":24,"roles":539,"affiliations":540,"properties":549},"1e1ac710-2aeb-4c70-9469-c17c874269a9",[140],[541],{"id":24,"sortIndex":25,"affiliation":542,"properties":24},{"id":543,"createTime":544,"updateTime":544,"relativeEntities":545,"slug":24,"properties":546,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"e13e2b6f-64c0-4397-9a59-ea48b96c0d00","2024-01-12T10:07:03.899+00:00",[],{"title":547},{"VI":548},"Institute of Image Information Technology and Engineering, Harbin Institute of Technology, Heilongjiang, Harbin 150001, PR China",{"title":550},{"VI":551},"Bing Zhu",{"id":553,"sortIndex":102,"researcher":24,"roles":554,"affiliations":555,"properties":564},"8b307c4d-34e1-49e1-a6a5-2e0d3b9f183b",[140],[556],{"id":24,"sortIndex":25,"affiliation":557,"properties":24},{"id":558,"createTime":559,"updateTime":559,"relativeEntities":560,"slug":24,"properties":561,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"f47b2a5d-1e13-4204-93c1-90810874be92","2024-01-12T10:07:03.913+00:00",[],{"title":562},{"VI":563},"Key laboratory of Remote Sensing Image Processing, Electronic Engineering College, Heilongjiang University, Harbin 150080, PR China",{"title":565},{"VI":566},"Chengxiao Qi",{"id":568,"sortIndex":138,"researcher":24,"roles":569,"affiliations":570,"properties":576},"9176c916-f457-4801-b601-d42bee420fed",[140],[571],{"id":24,"sortIndex":25,"affiliation":572,"properties":24},{"id":558,"createTime":559,"updateTime":559,"relativeEntities":573,"slug":24,"properties":574,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},[],{"title":575},{"VI":563},{"title":577},{"VI":578},"Ce Sun",{"id":580,"sortIndex":25,"researcher":24,"roles":581,"affiliations":582,"properties":588},"0e9837f2-33d6-42a5-ba3f-4e3b78790591",[140],[583],{"id":24,"sortIndex":25,"affiliation":584,"properties":24},{"id":558,"createTime":559,"updateTime":559,"relativeEntities":585,"slug":24,"properties":586,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},[],{"title":587},{"VI":563},{"title":589},{"VI":590},"Fuzhen Zhu",{"id":592,"sortIndex":155,"researcher":24,"roles":593,"affiliations":594,"properties":600},"7548f228-0626-463c-b979-d501372298fc",[140],[595],{"id":24,"sortIndex":25,"affiliation":596,"properties":24},{"id":558,"createTime":559,"updateTime":559,"relativeEntities":597,"slug":24,"properties":598,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},[],{"title":599},{"VI":563},{"title":601},{"VI":602},"Chen Wang",{"url":535,"publisher":604,"properties":634},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":605,"slug":10,"properties":606,"entityType":22,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25,"subjectFields":612,"manageAffiliations":613,"indexDatabases":614,"url":97,"thumbnailPath":24,"statistic":629,"gsStatistic":24,"type":24,"analyzePriority":24},[],{"country":607,"issn":608,"introduce":609,"eissn":610,"title":611},{"VOID":13},{"VOID":15},{"EN":17},{"VOID":19},{"EN":21},[],[],[615,622],{"id":59,"indexDatabase":616,"url":72,"indexYears":73,"academicFieldIds":621,"indexDatabaseRanking":76},{"id":61,"createTime":62,"updateTime":63,"relativeEntities":617,"label":618,"description":619,"key":69,"publicationTags":620,"standard":24},[],{"EN":66,"VI":66},{"EN":66,"VI":68},[71],[75],{"id":78,"indexDatabase":623,"url":93,"indexYears":24,"academicFieldIds":628,"indexDatabaseRanking":24},{"id":80,"createTime":81,"updateTime":82,"relativeEntities":624,"label":625,"description":626,"key":89,"publicationTags":627,"standard":24},[],{"EN":85,"VI":85},{"VI":87,"EN":88},[91,92],[95,96],{"impactFactor":25,"impactFactorByYear":630,"i10Index":25,"i10IndexLast5Year":25,"totalPublication":100,"totalPublicationByYear":631,"totalCitation":25,"totalCitationByYear":632,"totalCitationPerPublication":25,"totalCitationPerPublicationByYear":633,"hindexLast5Year":25,"hindex":25},{},{"2010":102,"2011":103,"2012":104,"2013":105,"2014":102,"2015":105,"2016":106,"2017":102,"2018":107,"2019":106,"2020":108,"2021":109,"2022":110,"2023":111},{},{},{"volume":635,"pages":636},{"VOID":323},{"VOID":637},"151-160",{"id":639,"createTime":640,"updateTime":641,"relativeEntities":642,"slug":643,"properties":644,"entityType":131,"verifyStatus":132,"verifyTime":641,"verifyNote":133,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25,"primaryUrl":651,"fullTextUrl":24,"authors":652,"publicationType":190,"publisherRelationship":670,"citationCount":24,"citationInfo":24,"publishDate":705,"publishYear":327,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":24,"openAccess":24,"references":24,"isForceReanalyzing":229},"97a3f4d9-ac25-41b6-92a8-fe9494680df1","2024-01-14T10:02:31.609+00:00","2024-12-30T23:39:02.861+00:00",[],"Adaptive-window-based-collaborative-representation-for-hyperspectral-anomaly-detection-with-fusion-of-local-and-global-information",{"references":645,"title":647,"doi":649},{"VOID":646},"Chen, 2022, Global to local: A hierarchical detection algorithm for hyperspectral image target detection, IEEE Trans. Geosci. Remote Sens., 60, 1\nChoi, 2016, Subsampling-based acceleration of simple linear iterative clustering for superpixel segmentation, Comput. Vis. Image Underst., 146, 1, 10.1016\u002Fj.cviu.2016.02.018\nDu, 2016, A spectral-spatial based local summation anomaly detection method for hyperspectral images, Signal Process., 124, 115, 10.1016\u002Fj.sigpro.2015.09.037\nElkholy, 2022, Unsupervised hyperspectral band selection with deep autoencoder unmixing, Int. J. Image Data Fusion, 13, 244, 10.1080\u002F19479832.2021.1972047\nFeng, 2022, Hyperspectral anomaly detection with total variation regularized low rank tensor decomposition and collaborative representation, IEEE GRSL, 19, 1\nGakhar, 2021, Spectral – spatial urban target detection for hyperspectral remote sensing data using artificial neural network, Egypt. J. Remote Sens. Space Sci., 24, 173\nGuo, 2014, Weighted- RXD and linear filter-based RXD: Improving background statistics estimation for anomaly detection in hyperspectral imagery, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 7, 2351, 10.1109\u002FJSTARS.2014.2302446\nHou, 2022, Collaborative representation with background purification and saliency weight for hyperspectral anomaly detection, Science China Inf. Sci., 65, 10.1007\u002Fs11432-020-2915-2\nHu, 2022, Hyperspectral anomaly detection using deep learning: A review, Remote Sens. (Basel), 14, 1973, 10.3390\u002Frs14091973\nImani, 2017, RX anomaly detector with rectified background, IEEE GRSL, 14, 1313\nImani, 2018, Attribute profile based target detection using collaborative and sparse representation, Neurocomputing, 313, 364, 10.1016\u002Fj.neucom.2018.06.006\nImani, 2018, Anomaly detection using morphology-based collaborative representation in hyperspectral imagery, Eur. J. Remote Sens., 51, 457, 10.1080\u002F22797254.2018.1446727\nImani, 2018, Manifold structure preservative for hyperspectral target detection, Adv. Space Res., 61, 2510, 10.1016\u002Fj.asr.2018.02.027\nImani, 2020, Sparse and collaborative representation-based anomaly detection, SIViP, 14, 1573, 10.1007\u002Fs11760-020-01709-0\nKang, 2020, Hyperspectral image visualization with edge-preserving filtering and principal component analysis, Information Fusion, 57, 130, 10.1016\u002Fj.inffus.2019.12.003\nKüçük, S., Yüksel, S.E., 2015. Comparison of RX-based anomaly detectors on synthetic and real hyperspectral data. 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Tokyo, Japan, pp. 1-4.\nLandgrebe, 1992, 220 Band Hyperspectral Image: AVIRIS Image Indian Pine Test Site 3, West Lafayette, Sch. Eng., Purdue Univ., Available Online\nLi, 2015, Collaborative representation for hyperspectral anomaly detection, IEEE Trans. Geosci. Remote Sens., 53, 1463, 10.1109\u002FTGRS.2014.2343955\nLi, 2020, Hyperspectral anomaly detection with kernel isolation forest, IEEE TGRS, 58, 319\nLi, 2023, Adaptively dictionary construction for hyperspectral target detection, IEEE Geosci. Remote Sens. Lett., 20, 5502005\nLiu, 2022, Multipixel anomaly detection with unknown patterns for hyperspectral imagery, IEEE Trans. Neural Networks Learn. Syst., 33, 5557, 10.1109\u002FTNNLS.2021.3071026\nLiu, 2022, Joint optimization of autoencoder and self-supervised classifier: anomaly detection of strawberries using hyperspectral imaging, Comput. Electron. Agric., 198, 107007, 10.1016\u002Fj.compag.2022.107007\nLu, 2023, Coupled adversarial learning for fusion classification of hyperspectral and LiDAR data, Information Fusion, 93, 118, 10.1016\u002Fj.inffus.2022.12.020\nMolero, 2013, Analysis and optimizations of global and local versions of the RX algorithm for anomaly detection in hyperspectral data, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 6, 801, 10.1109\u002FJSTARS.2013.2238609\nPaoletti, 2019, Deep learning classifiers for hyperspectral imaging: A review, ISPRS J. Photogramm. Remote Sens., 158, 279, 10.1016\u002Fj.isprsjprs.2019.09.006\nReed, 1990, Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution, IEEE Trans. Acoust. Speech Signal Process., 38, 1760, 10.1109\u002F29.60107\nSu, 2020, Low rank and collaborative representation for hyperspectral anomaly detection via robust dictionary construction, ISPRS J. Photogramm. Remote Sens., 169, 195, 10.1016\u002Fj.isprsjprs.2020.09.008\nTaghipour, 2016, Anomaly detection of hyperspectral imagery using differential morphological profile, 1219\nTan, 2019, Anomaly detection for hyperspectral imagery based on the regularized subspace method and collaborative representation, Remote Sens. (Basel), 11, 1318, 10.3390\u002Frs11111318\nWang, 2023, Hyperspectral anomaly detection using ensemble and robust collaborative representation, Inf. Sci., 624, 748, 10.1016\u002Fj.ins.2022.12.096\nWang, 2022, Hyperspectral anomaly detection via background purification and spatial difference enhancement, IEEE GRSL, 19, 1\nWang, 2022, Auto-AD: Autonomous hyperspectral anomaly detection network based on fully convolutional autoencoder, IEEE TGRS, 60, 1\nWu, 2018, Hyperspectral anomalous change detection based on joint sparse representation, ISPRS J. Photogramm. Remote Sens., 146, 137, 10.1016\u002Fj.isprsjprs.2018.09.005\nWu, 2022, Hyperspectral anomaly detection with relaxed collaborative representation, IEEE TGRS, 60, 1\nXiang, 2022, Hyperspectral anomaly detection with guided autoencoder, IEEE TGRS, 60, 1\nXiang, 2022, Hyperspectral anomaly detection with local correlation fractional Fourier transform and vector pulse coupled neural network, Infrared Phys. Technol., 127, 10.1016\u002Fj.infrared.2022.104430\nXiao, 2023, Anomaly detection of hyperspectral images based on transformer with spatial-spectral dual-window mask, IEEE JSTARS., 16, 1414\nYang, 2022, Ensemble and random RX with multiple features anomaly detector for hyperspectral image, IEEE GRSL, 19, 1\nZhang, G., Xu, M., Zhang, Y., Fan, Y., 2019. Improved Hyperspectral Anomaly Target Detection Method Based On Mean Value Adjustment, 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, pp. 1-4, Amsterdam, Netherlands.\nZhao, 2023, A joint method of spatial–spectral features and BP neural network for hyperspectral image classification, Egypt. J. Remote Sens. Space Sci., 26, 107",{"EN":648},"Adaptive window based collaborative representation for hyperspectral anomaly detection with fusion of local and global information",{"VOID":650},"10.1016\u002Fj.ejrs.2023.05.002","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1110982323000273",[653],{"id":654,"sortIndex":25,"researcher":24,"roles":655,"affiliations":656,"properties":667},"e4b71d66-5b88-49ef-b943-f19bf6e0a4b1",[140],[657],{"id":24,"sortIndex":25,"affiliation":658,"properties":24},{"id":659,"createTime":660,"updateTime":661,"relativeEntities":662,"slug":663,"properties":664,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"9973935c-714a-44ea-8c7a-7983dcea7b79","2023-12-07T22:33:04.366+00:00","2024-12-01T17:11:42.061+00:00",[],"Faculty-of-Electrical-and-Computer-Engineering-Tarbiat-Modares-university-Tehran-Iran",{"title":665},{"VI":666},"Faculty of Electrical and Computer Engineering, Tarbiat Modares university, Tehran, Iran",{"title":668},{"VI":669},"Maryam Imani",{"url":651,"publisher":671,"properties":701},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":672,"slug":10,"properties":673,"entityType":22,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25,"subjectFields":679,"manageAffiliations":680,"indexDatabases":681,"url":97,"thumbnailPath":24,"statistic":696,"gsStatistic":24,"type":24,"analyzePriority":24},[],{"country":674,"issn":675,"introduce":676,"eissn":677,"title":678},{"VOID":13},{"VOID":15},{"EN":17},{"VOID":19},{"EN":21},[],[],[682,689],{"id":59,"indexDatabase":683,"url":72,"indexYears":73,"academicFieldIds":688,"indexDatabaseRanking":76},{"id":61,"createTime":62,"updateTime":63,"relativeEntities":684,"label":685,"description":686,"key":69,"publicationTags":687,"standard":24},[],{"EN":66,"VI":66},{"EN":66,"VI":68},[71],[75],{"id":78,"indexDatabase":690,"url":93,"indexYears":24,"academicFieldIds":695,"indexDatabaseRanking":24},{"id":80,"createTime":81,"updateTime":82,"relativeEntities":691,"label":692,"description":693,"key":89,"publicationTags":694,"standard":24},[],{"EN":85,"VI":85},{"VI":87,"EN":88},[91,92],[95,96],{"impactFactor":25,"impactFactorByYear":697,"i10Index":25,"i10IndexLast5Year":25,"totalPublication":100,"totalPublicationByYear":698,"totalCitation":25,"totalCitationByYear":699,"totalCitationPerPublication":25,"totalCitationPerPublicationByYear":700,"hindexLast5Year":25,"hindex":25},{},{"2010":102,"2011":103,"2012":104,"2013":105,"2014":102,"2015":105,"2016":106,"2017":102,"2018":107,"2019":106,"2020":108,"2021":109,"2022":110,"2023":111},{},{},{"volume":702,"pages":703},{"VOID":323},{"VOID":704},"369-380","2023-08-01",{"id":707,"createTime":708,"updateTime":709,"relativeEntities":710,"slug":711,"properties":712,"entityType":131,"verifyStatus":132,"verifyTime":709,"verifyNote":133,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25,"primaryUrl":719,"fullTextUrl":24,"authors":720,"publicationType":190,"publisherRelationship":760,"citationCount":24,"citationInfo":24,"publishDate":796,"publishYear":797,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":24,"openAccess":24,"references":24,"isForceReanalyzing":229},"f940d4a5-c4ac-44c7-8d0a-3e3cd5d0a7b0","2023-12-19T07:52:57.743+00:00","2024-12-21T23:35:33.167+00:00",[],"Low-cost-framework-for-3D-reconstruction-and-track-detection-of-the-railway-network-using-video-data",{"references":713,"title":715,"doi":717},{"VOID":714},"Acharya, 2005\nAdham, 2020, Railway Tracks Detection of Railways Based on Computer Vision Technique and Gnss Data, 1\nAlshawabkeh, 2020, Linear Feature Extraction from Point Cloud Using Color Information, Heritage Sci., 8, 28, 10.1186\u002Fs40494-020-00371-6\nBay, 2008, Speeded-up Robust Features (SURF), Comput. Vis. Image Underst., 110, 346, 10.1016\u002Fj.cviu.2007.09.014\nBrown, Duane C., 1971. “Close-Range Camera Calibration,” no. January.\nChumachenko, 2013, Image Processing in UAV, 75\nEvagorou, 2021, Digital Camera Calibration for Cultural Heritage Documentation: The Case Study of a Mass Digitization Project of Religious Monuments in Cyprus, Eur. J. Remote Sens., 54, 6, 10.1080\u002F22797254.2020.1810131\nGabara, 2018, A New Approach for Inspection of Selected Geometric Parameters of a Railway Track Using Image-Based Point Clouds, Sensors (Switzerland), 18, 10.3390\u002Fs18030791\nGarcía-Luna, 2019, Structure from Motion Photogrammetry to Characterize Underground Rock Masses: Experiences from Two Real Tunnels, Tunnel. Underground Space Technol., 83, 262, 10.1016\u002Fj.tust.2018.09.026\nHough, Paul V C., 1962. “A Method and Means for Recognition Complex Patterns; US Patent: US3069654A.” US Patent, 6.\nInc., GoPro. 2016. “GoPro Hero 5 Black User Manual.” https:\u002F\u002Fgopro.com\u002Fcontent\u002Fdam\u002Fhelp\u002Fhero5-black\u002Fmanuals\u002F.\nKaleli, 2009, Vision-Based Railroad Track Extraction Using Dynamic Programming, IEEE Conf. Intelligent Transp. Syst., Proc., ITSC, 42–47\nKarakose, Mehmet, Orhan Yaman, Mehmet Baygin, Kagan Murat, and Erhan Akin. 2017. “A New Computer Vision Based Method for Rail Track Detection and Fault Diagnosis in Railways” 6 (1): 22–27. doi:10.18178\u002Fijmerr.6.1.22-27.\nKatiyar, S. K., Arun, P. V., 2014. “Comparative Analysis of Common Edge Detection Techniques in Context of Object Extraction” 50 (11): 68–79. http:\u002F\u002Farxiv.org\u002Fabs\u002F1405.6132.\nKholil, 2021, 3D Reconstruction Using Structure From Motion (SFM) Algorithm and Multi View Stereo (MVS) Based on Computer Vision, IOP Conf. Ser.: Mater. Sci. Eng., 1073, 10.1088\u002F1757-899X\u002F1073\u002F1\u002F012066\nKurkela, 2020, Applying Photogrammetry to Reconstruct 3D Luminance Point Clouds of Indoor Environments, Architect. Eng. Des. Manage., 1\nLeavers, 1992\nLi, 2014, Rail Component Detection, Optimization, and Assessment for Automatic Rail Track Inspection, IEEE Trans. Intelligent Transp. Syst., 15, 760, 10.1109\u002FTITS.2013.2287155\nLiu, 2019, A Review of Applications of Visual Inspection Technology Based on Image Processing in the Railway Industry, Transp. Saf. Environ., 1, 185, 10.1093\u002Ftse\u002Ftdz007\nLou, 2018, A Fast Algorithm for Rail Extraction Using Mobile Laser Scanning Data, Remote Sens. (Basel), 10\nMaire, 2010, Obstacle-Free Range Determination for Rail Track Maintenance Vehicles, 2172\nMiao, 2014, A Method for Accurate Road Centerline Extraction from a Classified Image, IEEE J. Selected Top. Appl. Earth Observ. Remote Sens., 7, 4762, 10.1109\u002FJSTARS.2014.2309613\nNeubert, M., Hecht, R., Gedrange, C., Trommler, M., Herold, H., Krüger, T., Brimmer, F., 2008. “Extraction of Railroad Objects From Very High Resolution Helicopter-Borne Lidar and Ortho-Image Data.” Commission VI, WG VI\u002F4, no. August 2008-Corpus ID: 8845018, Environmental Science.\nOude Elberink, 2013, Rail Track Detection and Modelling in Mobile Laser Scanner Data, ISPRS Ann. Photogrammetry, Remote Sens. Spatial Inf. Sci., 2, 223, 10.5194\u002Fisprsannals-II-5-W2-223-2013\nS Sai, Silvester, Martinus E Tjahjadi, and Catur A Rokhmana, 2019. “Geometric Accuracy Assessments of Orthophoto Production from UAV Aerial Images.” KnE Engineering, 2019:333–44. doi:10.18502\u002Fkeg.v4i3.5876.\nSaini, 2021, DroneRTEF: Development of a Novel Adaptive Framework for Railroad Track Extraction in Drone Images, Pattern Anal. Appl., 24, 1549, 10.1007\u002Fs10044-021-00994-w\nSingh, 2019, Vision Based Rail Track Extraction and Monitoring through Drone Imagery, ICT Express, 5, 250, 10.1016\u002Fj.icte.2017.11.010\nWestoby, 2012, ”Structure-from-Motion”- Photogrammetry: A Novel, Low-Cost Tool for Geomorphological Applications, Geomorphology, 179, 300, 10.1016\u002Fj.geomorph.2012.08.021\nWohlfeil, 2011, Vision Based Rail Track and Switch Recognition for Self-Localization of Trains in a Rail Network, IEEE Intelligent Vehicles Symposium, Proceedings, Iv, 1025\nZhang, Zhe, and Kin Hong Wong. 2014. “A NOVEL GEOMETRIC APPROACH FOR CAMERA CALIBRATION,” no. 2: 5806–10.\nZhang, Yesheng, Xu Zhao, and Dahong Qian, 2022. “Learning-Based Framework for Camera Calibration with Distortion Correction and High Precision Feature Detection.”\nZhang, Zhengyou. 1998. “A Flexible New Technique for Camera Calibration.” Technical Report MSR-TR-98-71. http:\u002F\u002Fresearch.microsoft.com\u002F̃zhang.",{"EN":716},"Low-cost framework for 3D reconstruction and track detection of the railway network using video data",{"VOID":718},"10.1016\u002Fj.ejrs.2022.11.001","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1110982322000928",[721,736,748],{"id":722,"sortIndex":155,"researcher":24,"roles":723,"affiliations":724,"properties":733},"e3f73f3a-2c17-4765-8067-837a7f9af5f7",[140],[725],{"id":24,"sortIndex":25,"affiliation":726,"properties":24},{"id":727,"createTime":728,"updateTime":728,"relativeEntities":729,"slug":24,"properties":730,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"cbe6a61c-80d3-4733-a807-da07ca206502","2023-12-19T07:52:57.770+00:00",[],{"title":731},{"VI":732},"Civil Engineering, Faculty of Engineering, Cairo University, Giza Governorate, 12613, Egypt",{"title":734},{"VI":735},"Mohamed Gomaa Mohamed",{"id":737,"sortIndex":103,"researcher":24,"roles":738,"affiliations":739,"properties":745},"5122f4dc-5f95-4de0-83b4-3eac13a843eb",[140],[740],{"id":24,"sortIndex":25,"affiliation":741,"properties":24},{"id":727,"createTime":728,"updateTime":728,"relativeEntities":742,"slug":24,"properties":743,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},[],{"title":744},{"VI":732},{"title":746},{"VI":747},"Adel El Shazly",{"id":749,"sortIndex":25,"researcher":24,"roles":750,"affiliations":751,"properties":757},"b08610b4-b5f2-4538-aac5-328d4dfb456d",[140],[752],{"id":24,"sortIndex":25,"affiliation":753,"properties":24},{"id":727,"createTime":728,"updateTime":728,"relativeEntities":754,"slug":24,"properties":755,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},[],{"title":756},{"VI":732},{"title":758},{"VI":759},"Adham Mahmoud",{"url":719,"publisher":761,"properties":791},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":762,"slug":10,"properties":763,"entityType":22,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25,"subjectFields":769,"manageAffiliations":770,"indexDatabases":771,"url":97,"thumbnailPath":24,"statistic":786,"gsStatistic":24,"type":24,"analyzePriority":24},[],{"country":764,"issn":765,"introduce":766,"eissn":767,"title":768},{"VOID":13},{"VOID":15},{"EN":17},{"VOID":19},{"EN":21},[],[],[772,779],{"id":59,"indexDatabase":773,"url":72,"indexYears":73,"academicFieldIds":778,"indexDatabaseRanking":76},{"id":61,"createTime":62,"updateTime":63,"relativeEntities":774,"label":775,"description":776,"key":69,"publicationTags":777,"standard":24},[],{"EN":66,"VI":66},{"EN":66,"VI":68},[71],[75],{"id":78,"indexDatabase":780,"url":93,"indexYears":24,"academicFieldIds":785,"indexDatabaseRanking":24},{"id":80,"createTime":81,"updateTime":82,"relativeEntities":781,"label":782,"description":783,"key":89,"publicationTags":784,"standard":24},[],{"EN":85,"VI":85},{"VI":87,"EN":88},[91,92],[95,96],{"impactFactor":25,"impactFactorByYear":787,"i10Index":25,"i10IndexLast5Year":25,"totalPublication":100,"totalPublicationByYear":788,"totalCitation":25,"totalCitationByYear":789,"totalCitationPerPublication":25,"totalCitationPerPublicationByYear":790,"hindexLast5Year":25,"hindex":25},{},{"2010":102,"2011":103,"2012":104,"2013":105,"2014":102,"2015":105,"2016":106,"2017":102,"2018":107,"2019":106,"2020":108,"2021":109,"2022":110,"2023":111},{},{},{"volume":792,"pages":794},{"VOID":793},"25",{"VOID":795},"1001-1012","2022-12-01",2022,{"id":799,"createTime":800,"updateTime":800,"relativeEntities":801,"slug":24,"properties":802,"entityType":131,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25,"primaryUrl":809,"fullTextUrl":24,"authors":810,"publicationType":190,"publisherRelationship":841,"citationCount":24,"citationInfo":24,"publishDate":877,"publishYear":878,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":24,"openAccess":24,"references":24,"isForceReanalyzing":229},"16b9e3df-bf15-472f-b8a5-8ddb70611a49","2023-12-06T23:34:11.060+00:00",[],{"references":803,"title":805,"doi":807},{"VOID":804},"Brivio, 2002, Integration of remote sensing data and GIS for accurate mapping of flooded areas, Int. J. Remote Sens., 23, 429, 10.1080\u002F01431160010014729\nCosta, 2004, Use of SAR satellites for mapping zonation of vegetation communities in the Amazon floodplain, Int. J. Remote Sens., 25, 1817, 10.1080\u002F0143116031000116985\nDewan, 2006, Using synthetic aperture radar (SAR) data for mapping river water flooding in an urban landscape: a case study of greater Dhaka, Bangladesh, J. Jpn. Soc. Hydrol. Water Resour., 19, 44, 10.3178\u002Fjjshwr.19.44\nGan, 2012, Flood mapping of Danube river at Romania using single and multi-date ERS2SAR images, Int. J. Appl. Earth Obs. Geoinf., 18, 69, 10.1016\u002Fj.jag.2012.01.012\nGong, 2001, Quantitative dynamic flood monitoring with NOAA AVHRR, Int. J. Remote Sens., 22, 1709, 10.1080\u002F01431160118481\nLanduyt, 2017, Pixel-based flood mapping from SAR imagery: a comparison of approaches, Geophys. Res. Abstr., 19\nLiu, 2002, Dynamic monitoring and damage evaluation of flood in north-west Jilin with remote sensing, Int. J. Remote Sens., 23, 3669, 10.1080\u002F01431160010006953\nLong, 2014, Flood extent mapping for Namibia using change detection and thresholding with SAR, Environ. Res. Lett., 9, 10.1088\u002F1748-9326\u002F9\u002F3\u002F035002\nMartinis, 2009, Towards operational near real time flood detection using a split based automatic thresholding procedure on high resolution TerraSARX data, Nat. Hazards Earth Syst. Sci., 9, 303, 10.5194\u002Fnhess-9-303-2009\nMatgen, 2011, Towards an automated SAR-based flood monitoring system: lessons learned from two case studies, Phys. Chem. Earth Parts A\u002FB\u002FC, 36, 241, 10.1016\u002Fj.pce.2010.12.009\nMeenakshi, 2011, Performance of speckle noise reduction filters on active radar and SAR images, Int. J. Technol. Eng. Syst. (IJTES), 2, 111\nRahman, 2006, Flood inundation mapping and damage assessment using multi-temporal RADARSAT and IRS 1C LISS III image, Asian J. Geoinf., 6, 11\nRahman, 2007, Flood hazard zonation-a GIS aided multicriteria evaluation approach (MCE) with remotely sensed data, Int. J. Geoinf., 3, 25\nRahman, 2008, Remote sensing, spatial multi-criteria evaluation (SMCE) and analytical hierarchy process (AHP) in optimal cropping pattern planning for a flood prone area, J. Spat. Sci., 53, 161, 10.1080\u002F14498596.2008.9635156\nRahman, 2009, Soil erosion hazard evaluation-an integrated use of remote sensing, GIS and statistical approaches with biophysical parameters towards management strategies, Ecol. Modell., 220, 1724, 10.1016\u002Fj.ecolmodel.2009.04.004\nSchumann, 2008, Near real-time flood wave approximation on large rivers from space: application to the River Po Italy, Water Resour. Res., 46\nSong, 2007, Efficient water area classification using Radarsat-1 SAR imagery in a high relief mountainous environment, Photogramm. Eng. Remote Sens., 73, 285, 10.14358\u002FPERS.73.3.285\nVoigt, S., Martinis, S., Zwenzner, H., Hahmann, T., Twele1, A., Schneiderhan, T., 2008. Extraction of flood masks using satellite based very high resolution SAR data for flood management and modeling. Fourth International Symposium on Flood Defence: Managing Flood Risk, Reliability and Vulnerability Toronto, Ontario, Canada, May 6–8, 2008.",{"EN":806},"Detecting, mapping and analysing of flood water propagation using synthetic aperture radar (SAR) satellite data and GIS: A case study from the Kendrapara District of Orissa State of India",{"VOID":808},"10.1016\u002Fj.ejrs.2017.10.002","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1110982317301126",[811,826],{"id":812,"sortIndex":155,"researcher":24,"roles":813,"affiliations":814,"properties":823},"ae45a2de-6a4d-4258-b5b4-fc66908ef29c",[140],[815],{"id":24,"sortIndex":25,"affiliation":816,"properties":24},{"id":817,"createTime":818,"updateTime":818,"relativeEntities":819,"slug":24,"properties":820,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"c73b5fe1-5cc8-4bd6-b64c-41e7e5052493","2023-12-06T23:34:11.082+00:00",[],{"title":821},{"VI":822},"Water Resources Department, Indian Institute of Remote Sensing (IIRS), 4, Kalidas Road, Dehradun 248001, India",{"title":824},{"VI":825},"Praveen K. Thakur",{"id":827,"sortIndex":25,"researcher":24,"roles":828,"affiliations":829,"properties":838},"4fcf217e-8c7f-4186-a4a4-431c0df01300",[140],[830],{"id":24,"sortIndex":25,"affiliation":831,"properties":24},{"id":832,"createTime":833,"updateTime":833,"relativeEntities":834,"slug":24,"properties":835,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"2237ce04-e8ba-46ce-9141-5be6ee43629f","2024-01-24T10:24:49.841+00:00",[],{"title":836},{"VI":837},"Dept. of Geography and Environmental Studies, University of Rajshahi, Rajshahi 6205, Bangladesh",{"title":839},{"VI":840},"Md. Rejaur Rahman",{"url":809,"publisher":842,"properties":872},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":843,"slug":10,"properties":844,"entityType":22,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25,"subjectFields":850,"manageAffiliations":851,"indexDatabases":852,"url":97,"thumbnailPath":24,"statistic":867,"gsStatistic":24,"type":24,"analyzePriority":24},[],{"country":845,"issn":846,"introduce":847,"eissn":848,"title":849},{"VOID":13},{"VOID":15},{"EN":17},{"VOID":19},{"EN":21},[],[],[853,860],{"id":59,"indexDatabase":854,"url":72,"indexYears":73,"academicFieldIds":859,"indexDatabaseRanking":76},{"id":61,"createTime":62,"updateTime":63,"relativeEntities":855,"label":856,"description":857,"key":69,"publicationTags":858,"standard":24},[],{"EN":66,"VI":66},{"EN":66,"VI":68},[71],[75],{"id":78,"indexDatabase":861,"url":93,"indexYears":24,"academicFieldIds":866,"indexDatabaseRanking":24},{"id":80,"createTime":81,"updateTime":82,"relativeEntities":862,"label":863,"description":864,"key":89,"publicationTags":865,"standard":24},[],{"EN":85,"VI":85},{"VI":87,"EN":88},[91,92],[95,96],{"impactFactor":25,"impactFactorByYear":868,"i10Index":25,"i10IndexLast5Year":25,"totalPublication":100,"totalPublicationByYear":869,"totalCitation":25,"totalCitationByYear":870,"totalCitationPerPublication":25,"totalCitationPerPublicationByYear":871,"hindexLast5Year":25,"hindex":25},{},{"2010":102,"2011":103,"2012":104,"2013":105,"2014":102,"2015":105,"2016":106,"2017":102,"2018":107,"2019":106,"2020":108,"2021":109,"2022":110,"2023":111},{},{},{"volume":873,"pages":875},{"VOID":874},"21",{"VOID":876},"S37-S41","2018-07-01",2018,{"id":880,"createTime":881,"updateTime":881,"relativeEntities":882,"slug":24,"properties":883,"entityType":131,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25,"primaryUrl":890,"fullTextUrl":24,"authors":891,"publicationType":190,"publisherRelationship":922,"citationCount":24,"citationInfo":24,"publishDate":957,"publishYear":327,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":24,"openAccess":24,"references":24,"isForceReanalyzing":229},"de462ae5-0b6b-4b81-b56e-e8492bd80596","2024-01-28T23:34:05.243+00:00",[],{"references":884,"title":886,"doi":888},{"VOID":885},"Adler-Golden, 2005, Remote bathymetry of the littoral zone from AVIRIS, LASH and QuickBird imagery, IEEE Transactions on Geoscience and Remote Sensing, 43, 337, 10.1109\u002FTGRS.2004.841246\nAshphaq, 2022, Analysis of univariate linear, robust-linear, and non-linear machine learning algorithms for satellite-derived bathymetry in complex coastal terrain, Regional Studies in Marine Science, 56, 102678, 10.1016\u002Fj.rsma.2022.102678\nAshphaq, 2022, Evaluation and performance of satellite-derived bathymetry algorithms in turbid coastal water: a case study of Vengurla rocks, Indian Journal of Geo-Marine Sciences (IJMS), 51, 310\nBrando, 2009, A Physics Based Retrieval and Quality Assessment of Bathymetry from Suboptimal Hyperspectral Data, Remote Sensing of Environment, 113, 755, 10.1016\u002Fj.rse.2008.12.003\nBrisson, L., Wolfe, D. A. and Staley, M., 2014. Interferometric Swath Bathymetry for Large Scale Shallow Water Hydrographic Surveys Canadian Hydrographic Conference. 1 – 18.\nCasal, 2019, Assessment of empirical algorithms for bathymetry extraction using Sentinel-2 data, International Journal of Remote Sensing, 40, 2855, 10.1080\u002F01431161.2018.1533660\nCasal, 2020, Understanding satellite-derived bathymetry using Sentinel 2 imagery and spatial prediction models, GIScience & Remote Sensing, 57, 271, 10.1080\u002F15481603.2019.1685198\nChavez, 1996, Image-based atmospheric corrections - Revisited and improved, Photogramm. Eng. Remote Sens., 1996, 1025\nDekker, 2011, Intercomparison of Shallow Water Bathymetry, Hydro-optics, and Benthos Mapping Techniques in Australian and Caribbean Coastal Environment, Limnology and Oceanography: Methods, 9, 396\nEhses, 2015, Depth derivation using multispectral WorldView-2 satelliteimagery, Civ Environ Eng, 24–46\nElshazly, R.E., Armanuos, A.M., Zeidan, B.A. et al.and ELshemy, M., Evaluating remote sensing approaches for mapping the bathymetry of Lake Manzala, Egypt. Euro-Mediterr J Environ Integr 6, 77 (2021). https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs41207-021-00285-0.\nESA, 2015. SENTINEL-2 User Handbook Sentinel-2 User Handbook SENTINEL-2 User Handbook Title Sentinel -2 User Handbook SENTINEL-2 User Handbook 1–64.\nEugenio, 2022, High Resolution Satellite Bathymetry Mapping: Regression and Machine Learning Based Approaches, IEEE, Transactions on Geoscience and Remote Sensing,, 60, 1, 10.1109\u002FTGRS.2021.3135462\nGabr, 2020, PlanetScope and landsat 8 imageries for bathymetry mapping, Journal of Marine Science and Engineering, 8, 143, 10.3390\u002Fjmse8020143\nHedley, 2016, A Physics-Based Method for the Remote Sensing of Seagrasses, Remote Sensing of Environment, 174, 134, 10.1016\u002Fj.rse.2015.12.001\nHossen, H.; Khairy, M.; Ghaly, S.; Scozzari, A.; Negm, A.; Elsahabi, M. Bathymetric and Capacity Relationships Based on Sentinel-3 Mission Data for Aswan High Dam Lake, Egypt. Water 2022, 14, 711. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fw14050711. https:\u002F\u002Fwww.pifsc.noaa.gov\u002Flibrary\u002Fpubs\u002Ftech\u002FNOAA_Tech_Memo_PIFSC_46.pdf5.\nKanno, 2011, Shallow Water Bathymetry from Multispectral Satellite Images: Extensions of Lyzenga’s Method for Improving Accuracy Coast, Eng. J., 53, 431\nLee, 2022, Assessment of hydrological changes in inland water body using satellite altimetry and Landsat imagery: A case study on Tsengwen Reservoir, Journal of Hydrology: Regional Studies, 44, 101227\nLyzenga, 1985, Shallow-water Bathymetry Using Combined LiDAR and Passive Multispectral Scanner Data, International Journal of Remote Sensing, 6, 115, 10.1080\u002F01431168508948428\nLyzenga, 2006, Multispectral bathymetry using a simple physically based algorithm IEEE Trans, Geosci. Remote Sens., 44, 2251, 10.1109\u002FTGRS.2006.872909\nManessa, 2016, Satellite-Derived Bathymetry Using Random Forest Algorithm and Worldview-2 Imagery, Geoplanning: Journal of Geomatics and Planning, 3, 117\nMarcello, 2021, Advanced Processing of Multiplatform Remote Sensing Imagery for the Monitoring of Coastal and Mountain Ecosystems, IEEE Access, 9, 6536, 10.1109\u002FACCESS.2020.3046657\nMaulud, 2020, A review on linear regression comprehensive in machine learning, Journal of Applied Science and Technology Trends, 1, 140, 10.38094\u002Fjastt1457\nMelsheimer, 2001, Extracting bathymetry from multi-temporal SPOT images, Asian Conf Remote Sens, 58, 37\nMisra, 2018, Shallow Water Bathymetry Mapping Using Support Vector Machine (SVM) Technique and Multispectral Imagery, International Journal of Remote Sensing, 39, 4431, 10.1080\u002F01431161.2017.1421796\nMohamed, H., Negm, A., Zahran, M.,and Saavedra, O.C. 2015. Assessment of Artificial Neural Network for bathymetry estimation using High Resolution Satellite imagery in Shallow Lakes: Case Study El Burullus Lake. Eighteenth International Water Technology Conference, IWTC 18 Sharm El Sheikh, 12-14 March 2015.\nMudiyanselage, 2022, Satellite-derived bathymetry using machine learning and optimal Sentinel-2 imagery in South-West Florida coastal waters, GIScience & Remote Sensing, 59, 1143, 10.1080\u002F15481603.2022.2100597\nNegm, 2017, Nile river bathymetry by satellite remote sensing case study: Rosetta branch, The Nile River, 259, 10.1007\u002F698_2017_17\nPalmer, 2015, Remote sensing of inland waters: Challenges, progress and future directions, Remote Sensing of Environment, 157, 1, 10.1016\u002Fj.rse.2014.09.021\nPrayudha Hartanto, Yustisi Lumban-Gaol, and Ratna Sari Dewi, 2020. A Comparative Analysis to Model Bathymetry using Multisensor Satellite Imageries. IOP Conf. Series: Earth and Environmental Science 618 (2020) 012027.\nSagawa, 2019, Satellite Derived Bathymetry Using Machine Learning and Multi-Temporal Satellite Images, Remote Sensing, 10 11(10), 1155, 10.3390\u002Frs11101155\nStumpf, 2003, Determination of water depth with high-resolution satellite imagery over variable bottom types, Limnology and Oceanography, 48, 547, 10.4319\u002Flo.2003.48.1_part_2.0547\nSukmono, A., Aji, Amarrohman, F. J., Bashit, N., and Saputra, L. R., 2022. The Extraction of Near-Shore Bathymetry using Sentinel-2A Satellite Imagery: Algorithms and Their Modifications. TEM Journal. Volume 11, Issue 1, pages 150-158, ISSN 2217‐8309, DOI: 10.18421\u002FTEM111-17.\nTonion, 2020, A machine learning approach to multispectral satellite derived bathymetry, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3, 565, 10.5194\u002Fisprs-annals-V-3-2020-565-2020\nWestley, 2021, Satellite-derived bathymetry for maritime archaeology: Testing its effectiveness at two ancient harbours in the Eastern Mediterranean, Journal of Archaeological Science: Reports, 38, 103030\nWu, Z., Mao, Z., Shen, W., Yuan, D., Zhang, X. and Huang, H., 2022. Satellite-derived bathymetry based on machine learning models and an updated quasi-analytical algorithm approach. Optics Express, 30(10), pp.16773-16793.\nYang, 2022, Bathymetric mapping and estimation of water storage in a shallow lake using a remote sensing inversion method based on machine learning, International Journal of Digital Earth, 15, 789, 10.1080\u002F17538947.2022.2069873\nZhou, 2023, A Comparison of Machine Learning and Empirical Approaches for Deriving Bathymetry from Multispectral Imagery, Remote Sensing, 15, 393, 10.3390\u002Frs15020393",{"EN":887},"Potential of Using Machine Learning Regression Techniques to Utilize Sentinel Images for Bathymetry Mapping of Nile River",{"VOID":889},"10.1016\u002Fj.ejrs.2023.06.004","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1110982323000431",[892,907],{"id":893,"sortIndex":155,"researcher":24,"roles":894,"affiliations":895,"properties":904},"9ffb900f-3443-4f90-be7a-312959c4026b",[140],[896],{"id":24,"sortIndex":25,"affiliation":897,"properties":24},{"id":898,"createTime":899,"updateTime":899,"relativeEntities":900,"slug":24,"properties":901,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"801bc7c3-7a93-4259-8dd6-d7ea556f62a2","2024-01-28T23:34:05.270+00:00",[],{"title":902},{"VI":903},"National Water Research Center – GIS Unit, Cairo, Egypt",{"title":905},{"VI":906},"Nagwa El-Ashmawy",{"id":908,"sortIndex":25,"researcher":24,"roles":909,"affiliations":910,"properties":919},"8c9de323-229c-4ce1-aaba-a6ba0571f567",[140],[911],{"id":24,"sortIndex":25,"affiliation":912,"properties":24},{"id":913,"createTime":914,"updateTime":914,"relativeEntities":915,"slug":24,"properties":916,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"cb00f8b2-c03b-45f1-8366-71b58862726f","2023-12-12T19:49:00.583+00:00",[],{"title":917},{"VI":918},"Nile Research Institute, National Water Research Center, Cairo, Egypt",{"title":920},{"VI":921},"Noha Kamal",{"url":890,"publisher":923,"properties":953},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":924,"slug":10,"properties":925,"entityType":22,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25,"subjectFields":931,"manageAffiliations":932,"indexDatabases":933,"url":97,"thumbnailPath":24,"statistic":948,"gsStatistic":24,"type":24,"analyzePriority":24},[],{"country":926,"issn":927,"introduce":928,"eissn":929,"title":930},{"VOID":13},{"VOID":15},{"EN":17},{"VOID":19},{"EN":21},[],[],[934,941],{"id":59,"indexDatabase":935,"url":72,"indexYears":73,"academicFieldIds":940,"indexDatabaseRanking":76},{"id":61,"createTime":62,"updateTime":63,"relativeEntities":936,"label":937,"description":938,"key":69,"publicationTags":939,"standard":24},[],{"EN":66,"VI":66},{"EN":66,"VI":68},[71],[75],{"id":78,"indexDatabase":942,"url":93,"indexYears":24,"academicFieldIds":947,"indexDatabaseRanking":24},{"id":80,"createTime":81,"updateTime":82,"relativeEntities":943,"label":944,"description":945,"key":89,"publicationTags":946,"standard":24},[],{"EN":85,"VI":85},{"VI":87,"EN":88},[91,92],[95,96],{"impactFactor":25,"impactFactorByYear":949,"i10Index":25,"i10IndexLast5Year":25,"totalPublication":100,"totalPublicationByYear":950,"totalCitation":25,"totalCitationByYear":951,"totalCitationPerPublication":25,"totalCitationPerPublicationByYear":952,"hindexLast5Year":25,"hindex":25},{},{"2010":102,"2011":103,"2012":104,"2013":105,"2014":102,"2015":105,"2016":106,"2017":102,"2018":107,"2019":106,"2020":108,"2021":109,"2022":110,"2023":111},{},{},{"volume":954,"pages":955},{"VOID":323},{"VOID":956},"545-555","2023-12-01",{"id":959,"createTime":960,"updateTime":961,"relativeEntities":962,"slug":963,"properties":964,"entityType":131,"verifyStatus":132,"verifyTime":961,"verifyNote":133,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25,"primaryUrl":971,"fullTextUrl":24,"authors":972,"publicationType":190,"publisherRelationship":1002,"citationCount":24,"citationInfo":24,"publishDate":1037,"publishYear":228,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":24,"openAccess":24,"references":24,"isForceReanalyzing":229},"ad9e80b0-ff91-423c-8541-8fe8310db7ef","2024-01-03T13:30:19.276+00:00","2024-12-08T23:31:42.639+00:00",[],"Comparison-of-support-vector-machine-and-neutral-network-classification-method-in-hyperspectral-mapping-of-ophiolite-m%C3%A9langes-A-case-study-of-east-of-Iran",{"references":965,"title":967,"doi":969},{"VOID":966},"AbdelRahman, 2016, Assessment of land suitability and capability by integrating remote sensing and GIS for agriculture in Chamarajanagar district, Karnataka, India, Egypt. J. Remote Sens. Space Sci., 19\nAbou El-Magd, 2014, Quantitative hyperspectral analysis for characterization of the coastal water from Damietta to Port Said, Egypt, Egypt. J. Remote Sens. Space Sci., 17, 61\nAhmadi Nadushan, 2009, Arak area land mapping using Neutral network and maximum likelihood classification methods, J. Iran. Nat. Geogr. Surv., 69, 83\nAlavipanah, 2003, 243\nAlavipanah, 2009, 385\nArvin, 1994, The petrogenesis and tectonic setting of lavas from the Baft ophiolitic mélange, southwest of kerman, Iran, Canad. J. Earth Sci., 31, 824, 10.1139\u002Fe94-076\nBeiranvand Pour, 2011, The Earth Observing-1 (EO-1) satellite data for geological mapping, southeastern segment of the Central Iranian Volcanic Belt, Iran, Int. J. Phys. Sci., 6, 7638\nBindschadler, 2003, Characterizing and correcting hyperion detectors using ice-sheet images, IEEE Trans. Geosci. Remote Sens., 41, 1189, 10.1109\u002FTGRS.2003.813208\nBrocker, M., Fotoohi Rad, G. R. and Thunissen, S., 2011. New time constraints for HP metamorphism and exhumation of mélange rocks from the Sistan suture zone, eastern Iran. An abstract paper in Turkey Symposium: Tectonic Crossroads: Evolving Orogens of Eurasia – Africa – Arabia.\nBröckera, 2013, New age constraints for the geodynamic evolution of the Sistan Suture Zone, eastern Iran, Lithos, 170–171, 17, 10.1016\u002Fj.lithos.2013.02.012\nBucher, 1994, 318\nClark, R.N., G.A. Swayze, A.J. Gallagher, T.V.V. King, and W.M. Calvin. 1993. The U. S. Geological Survey, Digital Spectral Library: Version 1: 0.2 to 3.0 microns, U.S. Geological Survey Open File Report 93–592, pp. 1340.\nClark, 1995, Automated spectral analysis: mapping minerals, amorphous minerals, environmental materials, vegetation, water, ice and snow, and other materials: the USGS tricorder algorithm (abstract), Lunar and Planetary Science XXVI, 255\nClark, 1990, Material absorption band depth mapping of imaging spectrometer data using a complete band shape least-squares fit with library reference spectra, 176\nCoops, N.C., Smith, M.L., Martin, M.E., Ollinger, S.V., Held, A.A. 2002. Predicting Eucalypt biochemistry from HYPERION and HYMAP im- agery. In: Proc. IGARSS, Toronto, ON, Canada.\nCrowley, 1992, Aviris study of Death Valley evaporate deposits using least squares band-fitting methods, JPL Publ., 92–14, 29\nDatt, 2003, Preprocessing EO-1 hyperion hyperspectral data to support the application of agricultural indexes, IEEE Trans. Geosci. Remote Sens., 41, 1246, 10.1109\u002FTGRS.2003.813206\nFelde, G.W., Anderson, G.P., Adler-Golden, S.M., Matthew, N.W., Berk, A. 2003. Analysis of hyperion data with the FLAASH atmospheric correction algorithm. In: Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS). Toulouse, 21–25 July 2003, pp. 90–92.\nFotoohi Rad, 2009, Early cretaceous exhumation of High-Pressure Metamorphic rocks of the Sistan Suture Zone, eastern Iran, Geol. J.\nFotouhirad, 1996, 230\nFotouhirad, 2004, 323\nGenderen, 1978, Remote sensing: statistical testing of thematic map accuracy, Remote Sens. Environ., 7, 3, 10.1016\u002F0034-4257(78)90003-2\nGersman, 2008, Mapping of hydrothermally altered rocks by the EO-1Hyperion sensor, northern Danakil Depression, Eritrea, Int. J. Remote Sens., 29, 3911, 10.1080\u002F01431160701874587\nGhazi, 2004, Geochemical characteristics, 40Ar\u002F39Ar ages and original tectonic setting of the Band-e Zeyar-at Anar ophilite, Makran accretionary prism, S.E. Iran, Tectonophysics, 393, 175, 10.1016\u002Fj.tecto.2004.07.035\nGilbert, 1997, 985\nGomes-Pugnair, 2003, The amphibolite from the Ossena-Morena \u002FCentral Iberian Variscan suture (Southwestern Iberian Massif): geochemistry and tectonic interpretation, Lithos, 68, 23, 10.1016\u002FS0024-4937(03)00018-5\nGoodarziMehr, 2012, Compared to the maximum likelihood method, support vector machine and neural network methods for the separation of lithological units, Q. Geol. Surv. Iran, 6, 75\nGoodenough, 2003, Processing Hyperion and ALI for forest classification, IEEE Trans. Geosci. Remote Sens., 41, 1321, 10.1109\u002FTGRS.2003.813214\nGupta, 2003, 654\nHonarmand, 2012, Application of spectral analysis in mapping hydrothermal alteration of the northwestern part of the Kerman cenozoic magmatic arc, Iran, J. Sci., Islam. Repub. Iran, 22, 221\nHubbard, 2003, Comparative alteration mineral mapping using visible to shortwave infrared (0.4–2.4μm) hyperion, ALI, and ASTER imagery, IEEE Trans. Geosci. Remote Sens., 41, 1401, 10.1109\u002FTGRS.2003.812906\nKhurshid, 2006, Preprocessing of EO-1 hyperion data, Can. J. Remote Sens., 32, 84, 10.5589\u002Fm06-014\nKruse, 1993, The spectral image processing system (SIPS) – interactive visualization and analysis of imaging spectrometer data: remote sensing of environment, 44, 145\nKruse, 2002, Comparison of EO-1 Hyperion and airborne hyperspectral remote sensing data for geologic applications\nKruse, 2003, Preliminary Results – Hyperspectral mapping of coral reef systems using EO-1Hyperion, Buck Island, U.S. Virgin Islands, 157\nKruse, 2003, Comparison of airborne hyperspectral data and EO-1 hyperion for mineral mapping, IEEE Trans. Geosci. Remote Sens., 41, 1388, 10.1109\u002FTGRS.2003.812908\nLeverington, 2008\nMantero, 2005, Partially supervised classification of remote sensing images through SVM-based probability density estimation, IEEE Trans. Geosci. Remote Sens., 43, 559, 10.1109\u002FTGRS.2004.842022\nMoeinzadeh, 2013, Application of support vector machine method in hyperspectral mapping of ophiolite mélanges-a case study from eastern Iran, J. Tethys, 1, 315\nMountrakis, 2011, Support vector machines in remote sensing: A review, ISPRS J. Photogr. Remote Sens., 13, 247, 10.1016\u002Fj.isprsjprs.2010.11.001\nPearlman, 2003, Hyperion, a space borne imaging spectrometer, IEEE Trans. Geosci. Remote Sens., 41, 1160, 10.1109\u002FTGRS.2003.815018\nRamadan, 2010, Characterization of gold mineralization in Garin Hawal area, Kebbi State, NW Nigeria, using remote sensing, Egypt. J. Remote Sens. Space Sci., 13, 153\nRamsey, 2004, Generation and validation of characteristic spectra from EO-1 Hyperion image data for detecting the occurrence of the invasive species, Chinese tallow, Int. J. Remote Sens., 26, 1611, 10.1080\u002F01431160512331326710\nRemote Sensing Tutorial of NASA, www.rst.gsfc.nasa.gov.\nRichards, 1999, 240\nRossetti, 2010, Early Cretaceous migmatitic mafic granulates from the Sabzevar range (NE Iran): implications for the closure of the Mesozoic peri-Tethyan oceans in central Iran, Terra Nova, 22, 26, 10.1111\u002Fj.1365-3121.2009.00912.x\nSabins, 1997\nSan, 2010, Evaluation of different atmospheric correction algorithms for eo-1 hyperion imagery, Int. Arch. Photogra. Remote Sens. Spatial Inform. Sci., XXXVIII, 392\nSarup, 2011, Comparison of QUAC and FLAASH atmospheric correction modules on EO-1 hyperion data of sanchi, Int. J. Adv. Eng. Sci. Technol., 4, 178\nShokr, 2011, Potential directions for applications of satellite earth observations data in Egypt, Egypt. J. Remote Sens. Space Sci., 14, 1\nSrivastava, 2009, Data classification using support vector machine, J. Theor. Appl. Inform. Technol., 1\nStaenz, 2002, Retrieval of surface reflectance from hyperion radiance data, IEEE Geosci. Remote Sens. Lett., 1, 1419\nTheunissen, S, Bröcker, M, Fotoohi Rad, Gh.R, 2011. HP metamorphism in the Sistan Suture Zone eastern Iran, New insights from Rb-Sr data. Symposium of Turkey.\nTirrul, 1983, The Sistan Suture zone of eastern Iran, Geol. Soc. Am. Bull., 94, 34, 10.1130\u002F0016-7606(1983)94\u003C134:TSSZOE>2.0.CO;2\nUSGS, 2004. Earth Observing 1, downloaded on May, 2009, from, Url: http:\u002F\u002Feo1.usgs.gov.\nVapnik, 1991, The necessary and sufficient conditions for consistency in the empirical risk minimization method, Pattern Recognit. Image Anal., 1, 283\nWeber Diefenbach, 1984, Paleozoic ophiolies in Iran, Geology and geochemistry, and geodynamic implication, Ofioliti, 11, 305\nWijaya, 2005\nZhang, 2008, A robust biased estimator for exterior orientation of pushbroom satellite imagery, Geomatica, 62, 455",{"EN":968},"Comparison of support vector machine and neutral network classification method in hyperspectral mapping of ophiolite mélanges–A case study of east of Iran",{"VOID":970},"10.1016\u002Fj.ejrs.2017.01.007","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1110982317300200",[973,990],{"id":974,"sortIndex":25,"researcher":24,"roles":975,"affiliations":976,"properties":987},"49ef6b79-5bc9-4f0d-a675-db5b97ca8a05",[140],[977],{"id":24,"sortIndex":25,"affiliation":978,"properties":24},{"id":979,"createTime":980,"updateTime":981,"relativeEntities":982,"slug":983,"properties":984,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},"0aeb332f-50fc-40c7-9d10-c9aaf4f71ab0","2024-01-03T13:30:19.284+00:00","2025-06-11T23:48:46.363+00:00",[],"Department-of-Geology-Shahid-Bahonar-University-of-Kerman-Iran",{"title":985},{"VI":986},"Department of Geology, Shahid Bahonar University of Kerman, Iran",{"title":988},{"VI":989},"Bahram Bahrambeygi",{"id":991,"sortIndex":155,"researcher":24,"roles":992,"affiliations":993,"properties":999},"57abb444-0488-4f19-965b-358fddaf074a",[140],[994],{"id":24,"sortIndex":25,"affiliation":995,"properties":24},{"id":979,"createTime":980,"updateTime":981,"relativeEntities":996,"slug":983,"properties":997,"entityType":44,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25},[],{"title":998},{"VI":986},{"title":1000},{"VI":1001},"Hesam Moeinzadeh",{"url":971,"publisher":1003,"properties":1033},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1004,"slug":10,"properties":1005,"entityType":22,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"syncStatus":23,"languages":24,"translateLanguages":24,"viewCount":25,"subjectFields":1011,"manageAffiliations":1012,"indexDatabases":1013,"url":97,"thumbnailPath":24,"statistic":1028,"gsStatistic":24,"type":24,"analyzePriority":24},[],{"country":1006,"issn":1007,"introduce":1008,"eissn":1009,"title":1010},{"VOID":13},{"VOID":15},{"EN":17},{"VOID":19},{"EN":21},[],[],[1014,1021],{"id":59,"indexDatabase":1015,"url":72,"indexYears":73,"academicFieldIds":1020,"indexDatabaseRanking":76},{"id":61,"createTime":62,"updateTime":63,"relativeEntities":1016,"label":1017,"description":1018,"key":69,"publicationTags":1019,"standard":24},[],{"EN":66,"VI":66},{"EN":66,"VI":68},[71],[75],{"id":78,"indexDatabase":1022,"url":93,"indexYears":24,"academicFieldIds":1027,"indexDatabaseRanking":24},{"id":80,"createTime":81,"updateTime":82,"relativeEntities":1023,"label":1024,"description":1025,"key":89,"publicationTags":1026,"standard":24},[],{"EN":85,"VI":85},{"VI":87,"EN":88},[91,92],[95,96],{"impactFactor":25,"impactFactorByYear":1029,"i10Index":25,"i10IndexLast5Year":25,"totalPublication":100,"totalPublicationByYear":1030,"totalCitation":25,"totalCitationByYear":1031,"totalCitationPerPublication":25,"totalCitationPerPublicationByYear":1032,"hindexLast5Year":25,"hindex":25},{},{"2010":102,"2011":103,"2012":104,"2013":105,"2014":102,"2015":105,"2016":106,"2017":102,"2018":107,"2019":106,"2020":108,"2021":109,"2022":110,"2023":111},{},{},{"volume":1034,"pages":1035},{"VOID":224},{"VOID":1036},"1-10","2017-06-01"]