A new spectral and spatial framework for detecting buildings with special roofing in hyperspectral images

Davood Akbari1
1Department of Surveying and Geomatics Engineering, College of Engineering, University of Zabol, Zabol, Iran

Tài liệu tham khảo

Akbari, 2020, Improving spectral-spatial classification of hyperspectral imagery by using extended minimum spanning forest algorithm, Can. J. Remote Sens., 46, 146, 10.1080/07038992.2020.1760714 Akbari, D., Saadatseresht, M., Homayouni, S., 2008. Evaluation of Different Hyperspectral Image Detection Methods. The 29th Asian Conference on Remote Sensing, Colombo, Sri Lanka. Akbari, 2014, A combination of spectral-spatial detection methods of hyperspectral images for the better separation of special buildings' roofs in urban area, J. Geomatics Sci. Techno., 4, 1 Akbari, D. 2017. A New Spectral-Spatial Framework for Classification of Hyperspectral Data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, GGT 2017, Kuala Lumpur, Malaysia. Bhattacharya, 2019, An overview of AVIRIS-NG airborne hyperspectral science campaign over India, Curr. Sci., 116, 1082, 10.18520/cs/v116/i7/1082-1088 Bradley, 1997, The use of the area under the ROC Curve in the evaluation of machine learning algorithms, Pattern Recognit., 30, 1145, 10.1016/S0031-3203(96)00142-2 Carvalho, O.A., Meneses, P.R., 2002. Spectral Correlation Mapper (SCM): An Improvement on the Spectral Angle Mapper (SAM). Asa Norte, 70910-900, Brasília, DF, Brasil. Chang, 2003 Chang, 2000, Constrained subpixel target detection for remotely sensed imagery, IEEE Trans. Geosci. Remote Sens., 38, 1144, 10.1109/36.843007 Chang, 2002, Anomaly detection and classification for hyperspectral imagery, IEEE Trans. Geosci. Remote Sens., 40, 1314, 10.1109/TGRS.2002.800280 Cheng, 2016, A survey on object detection in optical remote sensing images, ISPRS J. Photogramm. Remote Sens., 117, 11, 10.1016/j.isprsjprs.2016.03.014 Cristianini, 2000 Dos Reis Salles, 2017, Hyperspectral remote sensing applied to uranium exploration: A case study at the Mary Kathleen metamorphic-hydrothermal U-REE deposit, NW, Queensland, Australia, J. Geochem Explor., 179, 36, 10.1016/j.gexplo.2016.07.002 Du, Y., Chang, C.–I., Ren, H., 2004. New hyperspectral discrimination measure for spectral characterization. Opt. Eng., 43. Freitas, 2018, Hyperspectral imaging for real-time unmanned aerial vehicle maritime target detection, J. Intell Robot Syst., 90, 551, 10.1007/s10846-017-0689-0 Frolov, D., Smith, R.B., 1999. Locally Adaptive Constrained Energy minimization for AVIRIS image. Eighth JPL Airborne Earth Science (AVIRS). Gakhar, 2021, Spectral – spatial urban target detection for hyperspectral remote sensing data using artificial neural network, Egypt J. Remote Sens. Space Sci., 24, 173 Gonzalez, R.C., Woods, R.E., 2002. Digital Image Processing. Prentice Hall, 617 – 626. Homayouni, S., Roux, M., 2003. Hyperspectral image analysis for material mapping using spectral matching. ISPRS04-Istanbul, GET, Telecom Paris, UMR 5141 LTCI, Department TSI, 46 rue Barrault, 75013 Paris, France. Hou, Y., Zhang, Y., Yao, L., Liu, X., Wang, F., 2016. Mineral target detection based on MSCPE_BSE in hyperspectral image. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 1614–1617. Jang, 1993, ANFIS: adaptive-network-based fuzzy inference systems, IEEE Trans. Syst. Man Cybern Syst., 23, 665, 10.1109/21.256541 Jha, 2020, Gudalur Spectral Target Detection (GST-D): a new benchmark dataset and engineered material target detection in multi-platform remote sensing data, remote Sens., 12, 2145, 10.3390/rs12132145 Kanjir, 2018, Vessel detection and classification from space borne optical images: a literature survey, Remote Sens. Environ., 207, 1, 10.1016/j.rse.2017.12.033 Landgrebe, D., 1999. Some Fundamentals and methods for hyperspectral image Data Analysis. SPIE Photonics West, San Jose CA, 23-29. Li. X., Zhao, S., Rui, Y., Tang, W., 2007. An object-based classification approach for high-spatial resolution Imagery. Geoinformatics 2007: Remotely Sensed Data and Information, 6752. Prathap, G., Afanasyev, I., 2018. Deep Learning Approach for Building Detection in Satellite Multispectral Imagery. International Conference on Intelligent Systems (IS), Funchal, Portugal. Rajadell, O., Garćıa-Sevilla, P., Pla, F., 2009. Textural features for hyperspectral pixel classification. in IbPria09, Lecture Notes in Computer Science, 5524, 208-216. Ren, 2015, Faster R-CNN: towards real time object detection with region proposal networks, IEEE Trans. Pattern Anal., 39, 1137, 10.1109/TPAMI.2016.2577031 Rosenfield, 1986, A coefficient of agreement as a measure of thematic classification accuracy, Photogramm. Eng. Remote Sensing, 52, 223 Selvarajah, 2011, Analysis and comparison of texture features for content based image retrieval, IJLTC, 2, 108 Soille, P., 1999. Morphological Image Analysis: Principles and Applications. Springer-Verlag, 170-171. Stawiaski, J., 2008. Mathematical morphology and graphs: Application to interactive medical image segmentation. Ph.D. dissertation, Paris School Mines, Paris, France. Tarabalka, 2010, Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers, IEEE Trans. Syst. Man Cybern Syst., 40, 1267, 10.1109/TSMCB.2009.2037132 Tzotsos, 2006, A support vector machine approach for object based image analysis Van der Meer, 2006, The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery, Int. J. Appl. Earth Observation Geoinformation, 8, 3, 10.1016/j.jag.2005.06.001 Yadav, 2018, Parameters affecting target detection in VNIR and SWIR range, Egypt J. Remote Sens. Space Sci., 21, 325 Zhang, 2015, Importance of spatial and spectral data reduction in the detection of internal defects in food products, Appl. Spectrosc., 69, 473, 10.1366/14-07672