| Citation: |
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): 601-610.
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To fully utilize the spatial and spectral information of hyperspectral images and suppress image noise, a hyperspectral anomaly detection method based on local contrast and multidirectional gradient analysis is proposed. First, to leverage local spectral information, a local contrast strategy is introduced, generating a spectral detection score map based on the brightness difference between the target and the background. Then, to reduce computational complexity, a spectral fusion-based dimensionality reduction technique is proposed to process hyperspectral images. In addition, a local multidirectional gradient feature method is proposed to reduce image noise, retain local detail features, and generate a multidirectional gradient detection score map. Finally, the anomaly result map is obtained by fusing the spectral and gradient-based score graphs. Experimental results on four classical datasets demonstrate that the proposed method can successfully display abnormal targets in the result graph, achieving higher detection accuracy and lower false alarm rates compared to seven existing methods.
| [1] |
孙帮勇, 赵哲, 胡炳樑, 等. 基于3D卷积自编解码器和低秩表示的高光谱异常检测[J]. 光子学报, 2021, 50(4): 262-274.
SUN Bangyong, ZHAO Zhe, HU Bingliang, et al. Hyperspectral anomaly detection based on 3D convolutional autoencoder and low rank representation[J]. Acta Photonica Sinica, 2021, 50(4): 262-274.
|
| [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. DOI: 10.1109/JSTARS.2022.3167830
|
| [3] |
李茗欣, 黄远程, 竞霞, 等. 融合视觉注意机制的高光谱RX异常检测算法[J]. 红外技术, 2023, 45(4): 402-409. http://hwjs.nvir.cn/article/id/999a2986-db0e-4fe0-a22e-95fcc5a1b699
LI Mingxin, HUANG Yuancheng, JING Xia, et al. Hyperspectral RX anomaly detection algorithm with visual attention mechanism[J]. Infrared Technology, 2023, 45(4): 402-409. http://hwjs.nvir.cn/article/id/999a2986-db0e-4fe0-a22e-95fcc5a1b699
|
| [4] |
向英杰, 张俭峰, 杨桄, 等. 基于混合噪声估计的高光谱图像异常检测方法[J]. 红外技术, 2017, 39(8): 734-739. http://hwjs.nvir.cn/article/id/hwjs201708011
XIANG Yingjie, ZHANG Jianfeng, YANG Guang, et al. A mixed-noise estimation-based anomaly detection method for hyperspectral image[J]. Infrared Technology, 2017, 39(8): 734-739. http://hwjs.nvir.cn/article/id/hwjs201708011
|
| [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] |
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. DOI: 10.1109/JSTARS.2021.3052968
|
| [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. DOI: 10.3390/rs12091401
|
| [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] |
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] |
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. DOI: 10.1109/29.60107
|
| [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. DOI: 10.1109/JSTARS.2013.2238609
|
| [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. DOI: 10.1109/JSTARS.2014.2302446
|
| [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. DOI: 10.1109/TGRS.2004.841487
|
| [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] |
LI S, WANG W, QI H, et al. Low-rank tensor decomposition based anomaly detection for hyperspectral imagery[C]//2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015: 4525-4529.
|
| [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] |
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. DOI: 10.1109/JSTSP.2011.2113170
|
| [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] |
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. DOI: 10.5594/j18173
|
| [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] |
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] |
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] |
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] |
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] |
Tu B, Li N, Liao Z, et al. Hyperspectral anomaly detection via spatial density background purification[J]. Remote Sensing, 2019, 11(22): 2618. DOI: 10.3390/rs11222618
|
| [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. DOI: 10.1109/JSTARS.2019.2926130
|
| [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] |
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.
|
| [29] |
钱晓亮, 曾银凤, 林生, 等. 融合自适应窗口显著性检测和改进超像素分割的高光谱异常检测[J]. 遥感学报, 2023, 27(12): 2748-2761.
QIAN Xiaoliang, ZENG Yinfeng, LIN Sheng, et al. Hyperspectral anomaly detection via combining adaptive window saliency detection and improved superpixel segmentation[J]. National Remote Sensing Bulletin, 2023, 27(12): 2748-2761.
|
Supported by: Beijing Renhe Information Technology Co., Ltd. 
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