Infrared Technology, Volume. 47, Issue 5, 601(2025)
Hyperspectral Anomaly Detection Based on Local Contrast and Multidirectional Gradients
[2] [2] XU Y, ZHANG L, DU B, et al. Hyperspectral anomaly detection based on machine learning: an overview[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022,15: 3351-3364.
[5] [5] SONG X, LING Z, WU L, et al. Hyperspectral image anomaly detection based on background reconstruction[J].Journal of System Simulation, 2020,32(7): 1287-1293.
[6] [6] XIANG P, SONG J, QIN H, et al. Visual attention and background subtraction with adaptive weight for hyperspectral anomaly detection[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021,14: 2270-2283.
[7] [7] ZHAO D, Asano Y, GU L, et al. City-scale distance sensing via bispectral light extinction in bad weather[J].Remote Sensing, 2020,12(9): 1401.
[8] [8] 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.
[9] [9] ZHANG J, XU X, YAN W, et al. Hyperspectral anomaly detection based on local contrast estimation and sub-block background estimation[J].Infrared Physics & Technology, 2023,135: 104966.
[10] [10] 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.
[11] [11] 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.
[12] [12] GUO Q, ZHANG B, RAN Q, et al. Weighted-RXD and linear filter-based RXD: Improving background statistics estimation for anomaly detection in hyperspectral imagery[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014,7(6): 2351-2366.
[13] [13] 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.
[14] [14] LIU G, LIN Z, YAN S, et al. Robust recovery of subspace structures by low-rank representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012,35(1): 171-184.
[15] [15] LI S, WANG W, QI H, et al. Low-rank tensor decomposition based anomaly detection for hyperspectral imagery[C]//2015IEEE International Conference on Image Processing(ICIP).IEEE, 2015: 4525-4529.
[16] [16] LI W, DU Q. Collaborative representation for hyperspectral anomaly detection[J].IEEE Transactions on Geoscience and Remote Sensing, 2014,53(3): 1463-1474.
[17] [17] CHEN Y, Nasrabadi N M, Tran T D. Sparse representation for target detection in hyperspectral imagery[J].IEEE Journal of Selected Topics in Signal Processing, 2011,5(3): 629-640.
[18] [18] WEI J, ZHANG J, XU Y, et al. Hyperspectral anomaly detection based on graph regularized variational autoencoder[J].IEEE Geosci. Remote Sens. Lett., 2022,19: 1-5.
[19] [19] Banks M S, Read J C A, Allison R S, et al. Stereoscopy and the human visual system[J].SMPTE Motion Imaging Journal, 2012,121(4): 24-43.
[20] [20] XIE W, FAN S, QU J, et al. Spectral distribution-aware estimation network for hyperspectral anomaly detection[J].IEEE Transactions on Geoscience and Remote Sensing, 2021,60: 1-12.
[21] [21] XU Y, WU Z, LI J, et al. Anomaly detection in hyperspectral images based on low-rank and sparse representation[J].IEEE Transactions on Geoscience and Remote Sensing, 2015,54(4): 1990-2000.
[22] [22] ZHANG Y, DU B, ZHANG L, 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, 2015,54(3): 1376-1389.
[23] [23] CHENG T, WANG B. Graph and total variation regularized low-rank representation for hyperspectral anomaly detection[J].IEEE Transactions on Geoscience and Remote Sensing, 2019,58(1): 391-406.
[24] [24] GUO T, HE L, LUO F, et al. Anomaly detection of hyperspectral image with hierarchical anti-noise mutual-incoherence-induced low-rank representation[J].IEEE Transactions on Geoscience and Remote Sensing, 2023,61: 1-13.
[25] [25] Tu B, Li N, Liao Z, et al. Hyperspectral anomaly detection via spatial density background purification[J].Remote Sensing, 2019, 11(22): 2618.
[26] [26] Song S, Zhou H, Yang Y, et al. Hyperspectral anomaly detection via convolutional neural network and low rank with density-based clustering[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(9): 3637-3649.
[27] [27] Xiang P, Ali S, Jung S K, et al. Hyperspectral anomaly detection with guided autoencoder[J].IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-18.
[28] [28] Arisoy S, Nasrabadi N M, Kayabol K. Unsupervised pixel-wise hyperspectral anomaly detection via autoencoding adversarial networks[J].IEEE Geoscience and Remote Sensing Letters, 2021, 19: 1-5.
Get Citation
Copy Citation Text
WU Li, XU Xingchen, WANG Yian, REN Jiahong, ZHANG Jiajia, ZHAO Dong, WANG Xinlei. Hyperspectral Anomaly Detection Based on Local Contrast and Multidirectional Gradients[J]. Infrared Technology, 2025, 47(5): 601