[1] Zha Z J, Hua X S, Mei T et al. Joint multi-label multi-instance learning for image classification. [C]∥2008 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2008, Anchorage, AK, USA. New York: IEEE, 4587384(2008).

     Zha Z J, Hua X S, Mei T et al. Joint multi-label multi-instance learning for image classification. [C]∥2008 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2008, Anchorage, AK, USA. New York: IEEE, 4587384(2008).

[2] Wang J, Chang C I. Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 44, 1586-1600(2006). http://ieeexplore.ieee.org/document/1634722/

     Wang J, Chang C I. Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 44, 1586-1600(2006). http://ieeexplore.ieee.org/document/1634722/

[3] Marconcini M, Camps-Valls G, Bruzzone L. A composite semisupervised SVM for classification of hyperspectral images[J]. IEEE Geoscience and Remote Sensing Letters, 6, 234-238(2009). http://ieeexplore.ieee.org/document/4768706/

     Marconcini M, Camps-Valls G, Bruzzone L. A composite semisupervised SVM for classification of hyperspectral images[J]. IEEE Geoscience and Remote Sensing Letters, 6, 234-238(2009). http://ieeexplore.ieee.org/document/4768706/

[4] Archibald R, Fann G. Feature selection and classification of hyperspectral images with support vector machines[J]. IEEE Geoscience and Remote Sensing Letters, 4, 674-677(2007). http://ieeexplore.ieee.org/document/4317520

     Archibald R, Fann G. Feature selection and classification of hyperspectral images with support vector machines[J]. IEEE Geoscience and Remote Sensing Letters, 4, 674-677(2007). http://ieeexplore.ieee.org/document/4317520

[5] Ma L, Crawford M M, Tian J W. Local manifold learning-based k-nearest-neighbor for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 4099-4109(2010).

     Ma L, Crawford M M, Tian J W. Local manifold learning-based k-nearest-neighbor for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 4099-4109(2010).

[6] Camps-Valls G, Bruzzone L. Kernel-based methods for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 43, 1351-1362(2005). http://ieeexplore.ieee.org/document/1433032/

     Camps-Valls G, Bruzzone L. Kernel-based methods for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 43, 1351-1362(2005). http://ieeexplore.ieee.org/document/1433032/

[7] Berge A. Schistad Solberg A H. Structured Gaussian components for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 44, 3386-3396(2006).

     Berge A. Schistad Solberg A H. Structured Gaussian components for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 44, 3386-3396(2006).

[8] Huang Y C, Liu Q S, Zhang S T et al. Image retrieval via probabilistic hypergraph ranking. [C]∥2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 13-18, 2010, San Francisco, CA, USA. New York: IEEE, 5540012(2010).

     Huang Y C, Liu Q S, Zhang S T et al. Image retrieval via probabilistic hypergraph ranking. [C]∥2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 13-18, 2010, San Francisco, CA, USA. New York: IEEE, 5540012(2010).

[9] Bu J J, Tan S L, Chen C et al. Music recommendation by unified hypergraph. [C]∥Proceedings of the international conference on Multimedia - MM'10, October 25-29, 2010, Firenze, Italy, 2010. New York: ACM, 391-400(2010).

     Bu J J, Tan S L, Chen C et al. Music recommendation by unified hypergraph. [C]∥Proceedings of the international conference on Multimedia - MM'10, October 25-29, 2010, Firenze, Italy, 2010. New York: ACM, 391-400(2010).

[10] Sohn Y, Rebello N S. Supervised and unsupervised spectral angle classifiers[J]. Photogrammetric Engineering & Remote Sensing, 68, 1271-1280(2002).

     Sohn Y, Rebello N S. Supervised and unsupervised spectral angle classifiers[J]. Photogrammetric Engineering & Remote Sensing, 68, 1271-1280(2002).

[11] Chang C C, Lin C J. LIBSVM: a library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology, 2, 27(2011). http://dl.acm.org/citation.cfm?doid=1961189.1961199

     Chang C C, Lin C J. LIBSVM: a library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology, 2, 27(2011). http://dl.acm.org/citation.cfm?doid=1961189.1961199

[12] Hu W, Huang Y Y, Wei L et al. Deep convolutional neural networks for hyperspectral image classification[J]. Journal of Sensors, 2015, 258619(2015). http://www.tandfonline.com/servlet/linkout?suffix=CIT0026&dbid=16&doi=10.1080%2F15481603.2018.1426091&key=10.1155%2F2015%2F258619

     Hu W, Huang Y Y, Wei L et al. Deep convolutional neural networks for hyperspectral image classification[J]. Journal of Sensors, 2015, 258619(2015). http://www.tandfonline.com/servlet/linkout?suffix=CIT0026&dbid=16&doi=10.1080%2F15481603.2018.1426091&key=10.1155%2F2015%2F258619

[13] Lee H, Kwon H. Going deeper with contextual CNN for hyperspectral image classification[J]. IEEE Transactions on Image Processing, 26, 4843-4855(2017). http://ieeexplore.ieee.org/document/7973178

     Lee H, Kwon H. Going deeper with contextual CNN for hyperspectral image classification[J]. IEEE Transactions on Image Processing, 26, 4843-4855(2017). http://ieeexplore.ieee.org/document/7973178

[14] Ran L Y, Zhang Y N, Wei W et al. A hyperspectral image classification framework with spatial pixel pair features[J]. Sensors, 17, 2421(2017). http://www.ncbi.nlm.nih.gov/pubmed/29065535

     Ran L Y, Zhang Y N, Wei W et al. A hyperspectral image classification framework with spatial pixel pair features[J]. Sensors, 17, 2421(2017). http://www.ncbi.nlm.nih.gov/pubmed/29065535