Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points

Bagan, 2005, Land cover classification from MODIS EVI times-series data using SOM neural network, International Journal of Remote Sensing, 26, 4999, 10.1080/01431160500206650 Bishop, 2006 Bovolo, 2010, A support vector domain method for change detection in multitemporal images, Pattern Recognition Letter, 31, 1148, 10.1016/j.patrec.2009.07.002 Breiman, 1984 Brown, 2000, Linear spectral mixture models and support vector machines for remote sensing, IEEE Transactions on Geoscience and Remote Sensing, 38, 2346, 10.1109/36.868891 Camps-Valls, 2005, Kernel-based methods for hyperspectral image classification, IEEE Transactions on Geoscience and Remote Sensing, 43, 1351, 10.1109/TGRS.2005.846154 Camps-Valls, 2004, Robust support vector method for hyperspectral data classification and knowledge discovery, IEEE Transactions on Geoscience and Remote Sensing, 42, 1530, 10.1109/TGRS.2004.827262 Camps-Valls, 2008, Kernel-based framework for multitemporal and multisource remote sensing data classification and change detection, IEEE Transactions on Geoscience and Remote Sensing, 46, 1822, 10.1109/TGRS.2008.916201 Carpenter, 1997, ART neural networks for remote sensing: Vegetation classification from Landsat TM and terrain data, IEEE Transactions on Geoscience and Remote Sensing, 35, 308, 10.1109/36.563271 Carrão, 2008, Contribution of multispectral and multitemporal information from MODIS images to land cover classification, Remote Sensing of Environment, 112, 986, 10.1016/j.rse.2007.07.002 Chen, 2004, A simple method for reconstructing a highquality NDVI time-series data set based on the Savitzky–Golay filter, Remote Sensing of Environment, 91, 332, 10.1016/j.rse.2004.03.014 Defries, 1994, NDVI-Derived land-cover classification at a Global-scale, International Journal of Remote Sensing, 15, 3567, 10.1080/01431169408954345 Foody, 1996, Relating the land-cover composition of mixed pixels to artificial neural network classification output, Photogrammetric Engineering and Remote Sensing, 62, 491 Foody, 2004, A relative evaluation of multiclass image classification by support vector machines, IEEE Transactions on Geoscience and Remote Sensing, 42, 1335, 10.1109/TGRS.2004.827257 Friedl, 2002, Global land cover mapping from MODIS: algorithms and early results, Remote Sensing of Environment, 83, 287, 10.1016/S0034-4257(02)00078-0 Giri, 2005, A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets, Remote Sensing of Environment, 94, 123, 10.1016/j.rse.2004.09.005 Gong, 1990, An assessment of some factors influencing multispectral land-cover classification, Photogrammetric Engineering and Remote Sensing, 56, 597 Gopal, 1999, Fuzzy ARTMAP classification of global land cover from the 1 degree AVHRR data set, Remote Sensing of Environment, 67, 230, 10.1016/S0034-4257(98)00088-1 Gualtieri, J.A., Cromp, R.F., 1999. Support vector machines for hyperspectral remote sensing classification. In: Proc. SPIE 27th AIPR Workshop: Advances in Computer Assisted Recognition, Washington DC, 14–16 October, pp. 221–232. Hansen, 2003, Global percent tree cover at a spatial resolution of 500m: first results of the modis vegetation continuous fields algorithm, Earth Interactions, 7, 1, 10.1175/1087-3562(2003)007<0001:GPTCAA>2.0.CO;2 Homer, 2004, Development of a 2001 national land-cover database for the United States, Photogrammetric Engineering & Remote Sensing, 70, 829, 10.14358/PERS.70.7.829 Huang, 2002, An assessment of support vector machines for land cover classification, International Journal of Remote Sensing, 23, 725, 10.1080/01431160110040323 Huete, 2002, Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sensing of Environment, 83, 195, 10.1016/S0034-4257(02)00096-2 Joachims, 2002 Knight, 2006, Regional scale land cover characterization using MODIS-NDVI 250 m multi-temporal imagery: A phenology-based approach, Giscience & Remote Sensing, 43, 1, 10.2747/1548-1603.43.1.1 Lawrence, 2001, Rule-based classification systems using classification and regression tree (CART) analysis, Photogrammetric Engineering & Remote Sensing, 67, 1137 Liu, 2005, Comparison of non-linear mixture models: Sub-pixel classification, Remote Sensing of Environment, 94, 145, 10.1016/j.rse.2004.09.004 Lobell, 2004, Cropland distributions from temporal unmixing of MODIS data, Remote Sensing of Environment, 93, 412, 10.1016/j.rse.2004.08.002 Loveland, 2000, Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data, International Journal of Remote Sensing, 21, 1303, 10.1080/014311600210191 Lunetta, 2002, NALC/Mexico land cover mapping results: implications for assessing landscape condition, International Journal of Remote Sensing, 23, 3129, 10.1080/01431160110071888 Lunetta, 2010, Monitoring agricultural cropping patterns across the Laurentian Great Lakes Basin using MODIS-NDVI data, International Journal of Applied Earth Observation and Geoinformation, 12, 81, 10.1016/j.jag.2009.11.005 Lunetta, 2006, Land-cover change detection using multi-temporal MODIS NDVI data, Remote Sensing of Environment, 105, 142, 10.1016/j.rse.2006.06.018 Moody, 1996, Artificial neural network response to mixed pixels in coarse-resolution satellite data, Remote Sensing of Environment, 58, 329, 10.1016/S0034-4257(96)00107-1 Mountrakis, 2011, Support vector machines in remote sensing: A review, ISPRS Journal of Photogrammetry and Remote Sensing, 66, 247, 10.1016/j.isprsjprs.2010.11.001 Melgani, 2004, Classification of hyperspectral remote sensing images with support vector machines, IEEE Transactions on Geoscience and Remote Sensing, 42, 1778, 10.1109/TGRS.2004.831865 Nowak, 2010, Evaluating the National Land Cover Database tree canopy and impervious cover estimates across the conterminous United States: A comparison with photo-interpreted estimates, Environmental Management, 46, 378, 10.1007/s00267-010-9536-9 Pal, 2005, Support vector machines for classification in remote sensing, International Journal of Remote Sensing, 26, 1007, 10.1080/01431160512331314083 Paola, 1995, A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land-use classification, IEEE Transactions on Geoscience and Remote Sensing, 33, 981, 10.1109/36.406684 Quinlan, J.R., 1993. C4.5: programs for machine learning. Morgan Kaufmann Publishers, San Mateo, CA. Richards, 1999 Shao, 2010, Mapping cropland and major crop types across the Great Lakes Basin using MODIS-NDVI data, Photogrammetric Engineering & Remote Sensing, 76, 73, 10.14358/PERS.76.1.73 Shao, 2011, Sub-pixel mapping of tree canopy, impervious surfaces, and cropland in the Laurentian Great Lakes Basin using MODIS time-series data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4, 336, 10.1109/JSTARS.2010.2062173 Tasdemir, 2009, Exploiting data topology in visualization and clustering of self-organizing maps, IEEE Transactions on Neural Networks, 20, 549, 10.1109/TNN.2008.2005409 Townshend, 2002, Towards operational monitoring of terrestrial systems by moderate-resolution remote sensing, Remote Sensing of Environment, 83, 351, 10.1016/S0034-4257(02)00082-2 Vapnik, 1995 Vapnik, 1998 Venables, 1997 Wardlow, 2007, Analysis of time-series MODIS 250 m vegetation index data for crop classification in the US Central Great Plains, Remote Sensing of Environment, 108, 290, 10.1016/j.rse.2006.11.021 Wardlow, 2008, Large-area crop mapping using time-series MODIS 250 mNDVI data: An assessment for the US Central Great Plains, Remote Sensing of Environment, 112, 1096, 10.1016/j.rse.2007.07.019 Wickham, 2010, Thematic accuracy of the NLCD 2001 land cover for the conterminous United States, Remote Sensing of Environment, 114, 1286, 10.1016/j.rse.2010.01.018 Xavier, 2006, Multi-temporal analysis of MODIS data to classify sugarcane crop, International Journal of Remote Sensing, 27, 755, 10.1080/01431160500296735 Xiao, 2005, Mapping paddy rice agriculture in southern China using multi-temporal MODIS images, Remote Sensing of Environment, 95, 480, 10.1016/j.rse.2004.12.009 Zell, A., 1998. SNNS Stuttgart Network Simulator. User Manual, Version 4.2, University of Tübingen, Wilhem-Schickerd-Institute for Computer Science.