[1] [1] ZHAO D, CAO J L, ZHU X G, et al. Hyperspectral video target tracking based on deep edge convolution feature and improved context filter［J］. Remote Sensing, 2022, 14(24): 6219.

[2] [2] ZHAO D, ZHU X G, ZHANG Z, et al. Hyperspectral video target tracking based on pixel-wise spectral matching reduction and deep spectral cascading texture features［J］. Signal Processing, 2023, 209: 109033.

[3] [3] ZHAO D, ZHOU L, LI Y, et al. Visibility estimation via near-infrared bispectral real-time imaging in bad weather［J］. Infrared Physics & Technology, 2024, 136: 105008.

[4] [4] LI Z, ZHANG Y, ZHANG J P. Hyperspectral anomaly detection for spectral anomaly targets via spatial and spectral constraints［J］. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5511515.

[5] [5] FRONTERA-PONS J, VEGANZONES M A, PASCAL F, et al. Hyperspectral anomaly detectors using robust estimators［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(2): 720-731.

[6] [6] CHANG C I. Hyperspectral anomaly detection: a dual theory of hyperspectral target detection［J］. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5511720.

[7] [7] HORSTRAND P, DAZ M, GUERRA R, et al. A novel hyperspectral anomaly detection algorithm for real-time applications with push-broom sensors［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(12): 4787-4797.

[8] [8] REED I S, YU X. 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] [9] MOLERO J M, GARZON E M, GARCIA I, et al. Analysis and optimizations of global and local versions of the RX algorithm for anomaly detection in hyperspectral data［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(2): 801-814.

[10] [10] CARLOTTO M J. A cluster-based approach for detecting man-made objects and changes in imagery［J］. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(2): 374-387.

[11] [11] 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.

[12] [12] ZHOU J, KWAN C, AYHAN B, et al. A novel cluster kernel RX algorithm for anomaly and change detection using hyperspectral images［J］. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(11): 6497-6504.

[13] [13] ZHANG Y X, DU B, ZHANG L P, et al. A low-rank and sparse matrix decomposition-based Mahalanobis distance method for hyperspectral anomaly detection［J］. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(3): 1376-1389.

[14] [14] LI W, DU Q. Collaborative representation for hyperspectral anomaly detection［J］. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(3): 1463-1474.

[15] [15] WU Z Y, SU H J, TAO X W, et al. Hyperspectral anomaly detection with relaxed collaborative representation［J］. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5533417.

[16] [16] CHENG T K, WANG B. Graph and total variation regularized low-rank representation for hyperspectral anomaly detection［J］. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(1): 391-406.

[17] [17] GUO T, HE L, LUO F L, et al. Anomaly detection of hyperspectral image with hierarchical antinoise mutual-incoherence-induced low-rank representation［J］. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5510213.

[19] [19] MONTAVON G, LAPUSCHKIN S, BINDER A, et al. Explaining nonlinear classification decisions with deep Taylor decomposition［J］. Pattern Recognition, 2017, 65: 211-222.

[20] [20] ZHANG J J, XU X C, YAN W M, et al. Hyperspectral anomaly detection based on local contrast estimation and sub-block background estimation［J］. Infrared Physics & Technology, 2023, 135: 104966.

[21] [21] KANG X D, ZHANG X P, LI S T, et al. Hyperspectral anomaly detection with attribute and edge-preserving filters［J］. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(10): 5600-5611.

[22] [22] LU X Q, ZHANG W X, HUANG J. Exploiting embedding manifold of autoencoders for hyperspectral anomaly detection［J］. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(3): 1527-1537.