[2] Camps-Valls G, Gomez-Chova L, Munoz-Mari J, et al. Kernel-based framework for multitemporal and multisource remote sensing data classication and change detection [J]. IEEE Transactions on Geoscience and Remote Sensing(S0196-2892), 2008, 46(6): 1822–1835.

[3] Munoz-Mari J, Bruzzone L, Camps-Valls G. A support vector domain description approach to supervised classification of remote sensing images [J]. IEEE Transactions on Geoscience and Remote Sensing(S0196-2892), 2007, 45(8): 2683–2692.

[4] Tarabalka Y, Benediktsson J A, Chanussot J, et al. Multiple Spectral-Spatial Classification Approach for Hyperspectral Data [J]. IEEE Transactions on Geoscience and Remote Sensing(S0196-2892), 2010, 48: 4122-4132.

[5] LI Jun, Bioucas-Dias J M, Plaza A. Spectral-Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields [J]. IEEE Transactions on Geoscience and Remote Sensing(S0196-2892), 2012, 50: 809-823.

[6] Lee C H, Kuo B C, LIN C T, et al. A Spatial-Contextual Support Vector Machine for Remotely Sensed Image Classification [J]. IEEE Transactions on Geoscience and Remote Sensing(S0196-2892), 2012, 50: 784-799.

[7] Blum A, Mitchell T. Combining labeled and unlabeled data with co-training [C]//Proceedings of the Eleventh Annual Conference on Computational Learning Theory, Madison, Wisconsin, USA, July 24-26, 1998: 92-100.

[8] Jain A, Farrokhnia F. Unsupervised texture segmentation using Gabor Filters [J]. Pattern Recognition(S0031-3203), 1991, 24(12): 1167–1186.

[9] CHI M M, Bruzzone L. Semisupervised classication of hyperspectral images by svms optimized in the primal [J]. IEEE Transactions on Geoscience and Remote Sensing(S0196-2892), 2007, 45(6): 1870–1880.

[10] Melgani F, Bruzzone L. Classification of hyperspectral remote sensing images with support vector machines [J]. IEEE Transactions on Geoscience and Remote Sensing(S0196-2892), 2004, 42(8): 1778–1790.

[11] Archibald G F R. Feature selection and classification of hyperspectral images with support vector machines [J]. IEEE Geoscience and Remote Sensing Letters(S1545-598X), 2007, 4: 674–677.

[12] WU T, LIN C, WENG R. Probability estimates for multiclass classification by pairwise coupling [J]. The Journal of Machine Learning Research(S1532-4435), 2004, 5: 1005.

[13] AVIRIS NW indiana’s indian pines 1992 data set [OL]. ftp: //ftp.ecn.purdue.edu/biehl/MultiSpec/92AV3C.

[14] Neher R, Srivastava A. A Bayesian MRF Framework for Labeling Terrain Using Hyperspectral Imaging [J]. IEEE Transactions on Geoscience and Remote Sensing(S0196-2892), 2005, 43(6): 1363-1374.

[15] CHANG C C, LIN C J. LIBSVM: a library for support vector machines [OL](/2001). http: //www.csie.ntu.edu.tw/.cjlin/ libsvm.