[1] [1] SONG W, LI S, FANG L, et al. Hyperspectral image classification with deep feature fusion network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 99: 1-12.

[3] [3] CHEN Y, ZHAO X, JIA X. Spectral–spatial classification of hyperspectral data based on deep belief network[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2015, 8(6): 2381-2392.

[4] [4] ZHAO W, DU S. Learning multiscale and deep representations for classifying remotely sensed imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 113: 155-165.

[7] [7] Sabour S, Frosst N, Hinton G E. Dynamic routing between capsules[C]//Advances in Neural Information Processing Systems, 2017: 3856-3866.

[8] [8] Cohn D A, Ghahramani Z, Jordan M I. Active learning with statistical models[J]. Journal of Artificial Intelligence Research, 1996, 4(1):705-712.

[9] [9] Tuia D. Unbiased query-by-bagging active learning for VHR image classification[C]//Proceedings of SPIE, 2010, 7830: 78300K.

[10] [10] Quadrianto N, Petterson J, Smola A J. Distribution matching for transduction[C]//Advances in Neural Information Processing Systems, 2009: 1500-1508.

[12] [12] WANG J. Locally Linear Embedding[M]// Geometric Structure of High -Dimensional Data and Dimensionality Reduction, Springer Berlin Heidelberg, 2012: 203-220.