Semiautomatic approach for land cover classification: a remote sensing study for arid climate in southeastern Tunisia
Tóm tắt
Từ khóa
Tài liệu tham khảo
Adam E, Mutanga O, Odindi J, Abdel-Rahman EM (2014) Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers. Int J Remote Sens 35:3440–3458. doi: 10.1080/01431161.2014.903435
Bishop YMM, Fienberg SE, Holland PW et al (1977) Book review: discrete multivariate analysis: theory and practice. Appl Psychol Meas 1:297–306. doi: 10.1177/014662167700100218
Bouaziz M, Wijaya A, Gloaguen R (2011) Remote gully erosion mapping using ASTER data and geomorphologic analysis in the main Ethiopian rift. Geo Spat Inf Sci 14:246–254. doi: 10.1007/s11806-011-0565-1
Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2:121–167. doi: 10.1023/A:1009715923555
Castillejo-González IL, López-Granados F, García-Ferrer A et al (2009) Object- and pixel-based analysis for mapping crops and their agro-environmental associated measures using QuickBird imagery. Comput Electron Agric 68:207–215. doi: 10.1016/j.compag.2009.06.004
Felde GW, Anderson GP, Cooley TW, Matthew MW, Adler-Golden SM, Berk A, Lee J (2003) Analysis of hyperion data with the FLAASH atmospheric correction algorithm. IEEE Trans Geosci Remote Sen 3:90–92
Foody GM (2002) Status of land cover classification accuracy assessment. Remote Sens Environ 80(1):185–201
Gong P, Wang J, Yu L et al (2013) Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data. Int J Remote Sens 34:2607–2654. doi: 10.1080/01431161.2012.748992
Guermazi E, Bouaziz M, Zairi M (2016) Water irrigation management using remote sensing techniques: a case study in Central Tunisia. Environ Earth Sci 75:202. doi: 10.1007/s12665-015-4804-x
Gupta M, Srivastava PK (2010) Integrating GIS and remote sensing for identification of groundwater potential zones in the hilly terrain of Pavagarh, Gujarat, India. Water Int 35:233–245
Jensen JR (2005) Thematic map accuracy assessment. In: Introductory digital image processing: a remote sensing perspective, 3rd edn. Geographic Information Science Series. Prentice Hall, Upper Saddle River, pp 495–515
Jia K, Liang S, Zhang N et al (2014) Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data. ISPRS J Photogramm Remote Sens 93:49–55. doi: 10.1016/j.isprsjprs.2014.04.004
Kavzoglu T, Colkesen I (2009) A kernel functions analysis for support vector machines for land cover classification. Int J Appl Earth Obs Geoinf 11:352–359. doi: 10.1016/j.jag.2009.06.002
Kumar Y, Sahoo G (2012) Analysis of parametric & non parametric classifiers for classification technique using WEKA. Int J Inf Technol Comput Sci 4:43–49. doi: 10.5815/ijitcs.2012.07.06
Lu D, Mausel P, Batistella M, Moran E (2004) Comparison of land-cover classification methods in the Brazilian Amazon Basin. Photogramm Eng Remote Sens 70:723–731. doi: 10.14358/PERS.70.6.723
Manandhar R, Odeh IOA, Ancev T (2009) Improving the accuracy of land use and land cover classification of Landsat data using post-classification enhancement. Remote Sens 1:330–344. doi: 10.3390/rs1030330
Mantero P, Moser G, Serpico S (2005) Partially supervised classification of remote sensing images through SVM-based probability density estimation. IEEE Trans Geosci Remote Sens 43:559–570
Mountrakis G, Im J, Ogole C (2011) Support vector machines in remote sensing: a review. ISPRS J Photogramm Remote Sens 66:247–259. doi: 10.1016/j.isprsjprs.2010.11.001
Myint SW, Gober P, Brazel A et al (2011) Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens Environ 115:1145–1161. doi: 10.1016/j.rse.2010.12.017
Pal M, Mather PM (2004) Assessment of the effectiveness of support vector machines for hyperspectral data. Future Gener Comput Syst 20:1215–1225. doi: 10.1016/j.future.2003.11.011
Pal M, Mather PM (2005) Support vector machines for classification in remote sensing. Int J Remote Sens 26:1007–1011. doi: 10.1080/01431160512331314083
Paneque-Gálvez J, Mas J-F, Moré G et al (2013) Enhanced land use/cover classification of heterogeneous tropical landscapes using support vector machines and textural homogeneity. Int J Appl Earth Obs Geoinf 23:372–383. doi: 10.1016/j.jag.2012.10.007
Richards JA (1992) Remote sensing digital image analysis. Springer, Cambridge
Senf C, Leitão PJ, Pflugmacher D et al (2015) Mapping land cover in complex Mediterranean landscapes using Landsat: improved classification accuracies from integrating multi-seasonal and synthetic imagery. Remote Sens Environ 156:527–536. doi: 10.1016/j.rse.2014.10.018
Smits PC, Dellepiane SG, Schowengerdt RA (1999) Quality assessment of image classification algorithms for land-cover mapping: a review and proposal for a cost-based approach. Int J Remote Sens 20:1461–1486
Srivastava PK, Han D, Rico-Ramirez MA et al (2012) Selection of classification techniques for land use/land cover change investigation. Adv Space Res 50:1250–1265. doi: 10.1016/j.asr.2012.06.032
Tompkins S, Mustard JF, Forsyth DW (1986) Optimization of endmembers for spectral mixture analysis. Remote Sens Environ 59:472–489. doi: 10.1016/S0034-4257(96)00122-8
Vapnik V (1982) Estimation of dependences based on empirical data. Springer, New York
Wang L, Zhu J, Zou H (2006) The doubly regularized support vector machine. Stat Sin 16:589–615
Wijaya A, Marpu PR, Gloaguen R (2008) Geostatistics texture classification of tropical rainforest in Indonesia. In: Stein S, Shi W, Bijker W (eds) Quality aspects in spatial data mining. CRC, Boca Raton, pp 199–210