[3] SU H J, YANG H, DU Q, et al. Semisupervised band clustering for dimensionality reduction of hyperspectral imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(6): 1135-1139.
[4] BANDOS T V, BRUZZONE L, CAMPS-VALLS G. Classification of hyperspectral images with regularized linear discriminant analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(3): 862-873.
[5] ZABALZE J, REN J, ZHENG J, et al. Novel two-dimensional singular spectrum analysis for effective feature extraction and data classification in hyperspectral imaging[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(8): 1-16.
[6] FAUVEL M, CHANUSSOT J, BENEDIKTSSON J A. Kernel principa component analysis for the classification of hyperspectral remote sensing data over urban areas[J]. EURASIP Journal on Advances in Signal Processing, 2009, 2009(1): 1-14.
[7] LI J, HUANG X, GAMBA P, et al. Multiple feature learning for hyperspectral image classification[J]. IEEE Transactions on Geoscience & Remote Sensing, 2015, 53(3): 1592-1606.
[8] LI W, CHEN C, SU H J, et al. Local binary patterns and extreme learning machine for hyperspectral imagery classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(7): 3681-3693.
[11] HU W, HUANG Y Y, WEI L, et al. Deep convolutional neural networks for hyperspectral image classification[J]. Journal of Sensors, 2015, 2015(3): 258619.
[12] RASTI B, HONG D, HANG R, et al. Feature extraction for hyperspectral imagery: the evolution from shallow to deep: overview and toolbox[J]. IEEE Geoscience and Remote Sensing Magazine, 2020, 8(4): 60-88.
[13] XU X D, LI W, RAN Q, et al. Multisource remote sensing data classification based on convolutional neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 56(2): 937-949.
[14] HAMIDA A B, BENOIT A, LAMBERT P, et al.3-D deep learning approach for remote sensing image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(8): 4420-4434.
[16] LIU S J, SHI Q, ZHANG L P. Few-shot hyperspectral image classification with unknown classes using multitask deep learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(6): 5085-5102.
[17] ZHU L, CHEN Y S, GHAMSI P, et al. Generative adversarial networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(9): 5046-5063.
[19] HECKE W V, LEEMANS A, BACKER S D, et al. Comparing isotropic and anisotropic smoothing for voxelbased DTI analyses: a simulation study[J]. Human Brain Mapping, 2010, 31(1): 98-114.
[20] JIA S, ZHU Z X, SHEN L L, et al. A two-stage feature selection framework for hyperspectral image classification using few labeled samples[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(4): 1023-1035.
[21] ZHU Z X, JIA S, HE S, et al. Three-dimensional Gabor feature extraction for hyperspectral imagery classification using a memetic framework[J]. Information Sciences, 2015, 298(C): 274-287.
[22] JIA S, HU J, ZHU J S, et al. Three-dimensional local binary patterns for hyperspectral imagery classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(4): 2399-2413.
[23] JIA S, SHEN L L, ZHU J S, et al. A 3-D Gabor phase-based coding and matching framework for hyperspectral imagery classification[J]. IEEE Transactions on Cybernetics, 2017, 48(4): 1176-1188.
[26] TAN X, TRIGGS B. Enhanced local texture feature sets for face recognition under difficult lighting conditions[J]. IEEE Transactions on Image Processing, 2010, 19(6): 1635-1650.
[28] DAUGMAN J G. High confidence visual recognition of persons by a test of statistical independence[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993, 15(11): 1148-1161.