[1] ZHANG B, CHEN Z C, PENG D L, et al.Remotely sensed big data:evolution in model development for information extraction［J］.Proceedings of the IEEE, 2019, 107(12):2294-2301.
[2] REED I S, YU X L.Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution［J］.IEEE Transactions on Acoustics, Speech and Signal Processing, 1990, 38(10):1760-1770.
[9] KHAZAI S, HOMAYOUNI S, SAFARI A, et al.Anomaly detection in hyperspectral images based on an adaptive support vector method［J］.IEEE Geoscience and Remote Sensing Letters, 2011, 8(4):646-650.
[10] KWON H, NASRABADI N M.Kernel RX-algorithm:a nonlinear anomaly detector for hyperspectral imagery［J］.IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(2):388-397.
[11] CHENG B Z, ZHANG J P.Anomaly detection in hyperspectral imagery based on spectral dimensions transformation and spatial filter［J］.Journal of Applied Science and Engineering, 2017, 20(1):107-120.
[12] ZHAO C H, LI J W, MENG M L, et al.A weighted spatial-spectral kernel RX algorithm and efficient implementation on GPUs［J］.Sensors, 2017, 17(3):441.
[15] YUAN Z Z,SUN H, JI K F, et al.Local sparsity divergence for hyperspectral anomaly detection［J］.IEEE Geoscience and Remote Sensing Letters, 2014, 11(10):1697-1701.
[16] MA D D, YUAN Y, WANG Q.Hyperspectral anomaly detection via discriminative feature learning with multiple-dictionary sparse representation［J］.Remote Sensing, 2018, 10(5):745.
[17] YANG Y X, ZHANG J Q, SONG S Z, et al.Hyperspectral anomaly detection via dictionary construction-based low-rank representation and adaptive weighting［J］.Remote Sensing, 2019, 11(2):192.
[18] LING Q, GUO Y L, LIN Z P, et al.A constrained sparse representation model for hyperspectral anomaly detection［J］.IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(4):2358-2371.
[19] LUO J, GWUN O.A comparison of SIFT, PCA-SIFT and SURF［J］.International Journal of Image Processing, 2009, 3(4):143-152.
[20] HAO R R, SU Z X.A patch-based low-rank tensor approximation model for multiframe image denoising［J］.Journal of Computational and Applied Mathematics, 2018, 329(1):125-133.
[21] FAN H Y, CHEN Y J, GUO Y L, et al.Hyperspectral ima-ge restoration using low-rank tensor recovery［J］.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(10):4589-4604.
[22] ZHANG J P, TAN J, ZHANG Y.Joint sparse tensor representation for the target detection of polarized hyperspectral images［J］.IEEE Geoscience and Remote Sensing Letters, 2017, 14(12):2235-2239.
[23] GENG X R, SUN K, JI L Y, et al.A high-order statistical tensor based algorithm for anomaly detection in hyperspectral imagery［J］.Scientific Reports, 2014, 4:6869.
[24] ZHANG L L, ZHAO C H.Tensor decomposition-based sparsity divergence index for hyperspectral anomaly detection［J］.Journal of the Optical Society of America A, 2017, 34(9):1585-1594.
[25] XU Y, WU Z B, CHANUSSOT J, et al.Joint reconstruc-tion and anomaly detection from compressive hyperspec-tral images using mahalanobis distance-regularized tensor RPCA［J］.IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(5):2919-2930.
[26] ZHANG L L, CHENG B Z, DENG Y W.A tensor-based adaptive subspace detector for hyperspectral anomaly detection［J］.International Journal of Remote Sensing, 2018, 39(8):2366-2382.
[27] YUE J, MAO S J, LI M.A deep learning framework for hyperspectral image classification using spatial pyramid pooling［J］.IEEE Remote Sensing Letters, 2016, 7(9):875-884.
[28] NARENDRA G, SIVAKUMAR D.Deep learning based hyperspectral image analysis—a survey［J］.Journal of Computational and Theoretical Nanoscience, 2019, 16(4):1528-1535.
[29] LIU B, YU X C, YU A Z, et al.Deep few-shot learning for hyperspectral image classification［J］.IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(4):2290-2304.
[30] WEI L, WU G D, DU Q.Transferred deep learning for anomaly detection in hyperspectral imagery［J］.IEEE Remote Sensing Letters, 2017, 14(5):597-601.
