Hyperspectral RX Anomaly Detection Method Based on the Fusion of Spatial and Spectral Feature

doi:10.16182/j.issn1004731x.joss.21-FZ0780

Abstract

Abstract: To address the problem that the hyperspectral anomaly detection algorithm does not make full use of the spatial information of the hyperspectral image and the detection accuracy is limited, a FSSRX (Fusing Spatial and Spectral Reed-Xiaol) anomaly detection algorithm that fuses spatial and spectrum information is proposed to improve the accuracy of hyperspectral anomaly detection. In FSSRX algorithm, the spatial feature of hyperspectral images is firstly extracted by the EMAP(Extended Multi-attribute Profile) method and the abnormal score of each pixel in spatial features is then calculated with RX detector. Meanwhile, RX anomaly detection is carried out directly on the original hyperspectral image to calculate the abnormal score of each pixel in the spectral feature. The anomaly scores obtained in spatial and spectral features are effectively combined to improve the detection accuracy. Simulation results show that FSSRX algorithm can effectively improve the detection accuracy and reduce the false alarm rate. Compared with other algorithms, FSSRX algorithm can achieve better detection performance.

Key words: hyperspectral image (HSI), fusing spatial and spectral feature, EMAP, anomaly detection, RX detector

CLC Number:

Liu Xuan, Li Xiangyang, He Fang, Zhao Jianwei, Zhang Fenggan. Hyperspectral RX Anomaly Detection Method Based on the Fusion of Spatial and Spectral Feature[J]. Journal of System Simulation, 2021, 33(12): 2891-2900.

References

[1] 张良培. 高光谱遥感影像处理[M]. 北京: 科学出版社, 2014.
Zhang Liangpei.Hyperspectral Remote Sensing Image Processing[M]. Beijing: Science Press, 2014.
[2] 宋晓瑞, 邹玲, 吴玲达, 等. 基于背景重建的高光谱图像异常检测[J]. 系统仿真学报, 2020, 32(7): 1287-1293.
Song Xiaorui, Zou Ling, Wu Lingda, et al.Hyperspectral Image Anomaly Detection Based on Background Reconstruction[J]. Journal of System Simulation, 2020, 32(7): 1287-1293.
[3] 宋义刚, 吴泽彬, 孙乐, 等. 一种新的空谱联合稀疏高光谱目标检测方法[J]. 兵工学报, 2014, 35(6): 834-841.
Song Yigang, Wu Zebin, Sun Le, et al.A Novel Spectral-spatial Sparse Method for Hyperspectral Target Detection[J]. Acta Armamentarii, 2014, 35(6): 834-841.
[4] 刘一博. 基于背景数据优化的高光谱图像异常检测算法研究[D]. 哈尔滨: 哈尔滨工程大学, 2017.
Liu Yibo.The Research of Hyperspectral Anomaly Detection Algorithm Based on Background Data Optimized[D]. Harbin: Harbin Engineering University, 2017.
[5] 马世欣, 刘春桐, 李洪才, 等. 基于高光谱图像探测与感知的伪装效果评估方法[J]. 兵工学报, 2019, 40(7): 1485-1494.
Ma Shixin, Liu Chuntong,Li Hongcai, et al.Camouflage Effect Evaluation Based on Hyperspectral Image[J]. Detection and Visual Perception, 2019, 40(7): 1485-1494.
[6] 李肖. 高光谱异常检测算法研究[D]. 西安: 西安电子科技大学, 2015.
Li Xiao.Study on Anomaly Detection Algorithm of Hyperspectral Imagery[D]. Xi'an: Xidian University, 2015.
[7] 王鑫鹏. 基于背景纯化的高光谱异常目标检测研究[D]. 哈尔滨: 哈尔滨工程大学, 2017.
Wang Xinpeng.Research on Hyperspectral Remote Sensing Anomaly Detection Based on Background Refinement[D]. Harbin: Harbin Engineering University, 2017.
[8] Hosseiny B, Shah-Hosseini R.A Hyperspectral Anomaly Detection Framework Based on Segmentation and Convolutional Neural Network Algorithms[J]. International Journal of Remote Sensing (S0143-1161), 2020, 41(18): 6946-6975.
[9] 张香萍. 联合空间与光谱信息的高光谱图像异常目标检测方法研究[D]. 长沙: 湖南大学, 2018.
Zhang Xiangping.Hyperspectral Image Anomaly Detection Method by Fusing Spatial and Spectral Information[D]. Changsha: Hunan University, 2018.
[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 & Signal Processing (S0096-3518), 1990, 38(10): 1760-1770.
[11] Kwon H, Nasrabadi N M.Kernel RX-algorithm: a Nonlinear Anomaly Detector for Hyperspectral Imagery[J]. IEEE Transactions on Geoscience and Remote Sensing (S0196-2892), 2005, 43(2): 388-397.
[12] Carlotto M J.A Cluster-based Approach for Detecting Man-made Objects and Changes in Imagery[J]. IEEE Transactions on Geoence & Remote Sensing (S0196-2892), 2005, 43(2): 374-387.
[13] 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 & Remote Sensing (S1939-1404), 2014, 7(6): 2351-2366.
[14] Du B, Zhang L.Random-Selection-Based Anomaly Detector for Hyperspectral Imagery[J]. IEEE Transactions on Geoscience and Remote Sensing (S0196-2892), 2011, 49(5): 1578-1589.
[15] 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 (S0196-2892), 2016, 54(11): 1-8.
[16] Li W, Du Q.Collaborative Representation for Hyperspectral Anomaly Detection[J]. IEEE Transactions on Geoscience and Remote Sensing (S0196-2892), 2015, 53(3): 1463-1474.
[17] Sun W, Liu C, Li J, et al.Low-rank and Sparse Matrix Decomposition-based Anomaly Detection for Hyperspectral Imagery[J]. Journal of Applied Remote Sensing (S1931-3195), 2014, 8(1): 083641.
[18] 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 (S0196-2892), 2016, 54(3): 1376-1389.
[19] Dalla M M, Atli B J, Waske B, et al.Morphological Attribute Profiles for the Analysis of Very High Resolution Images[J]. IEEE Transactions on Geoence & Remote Sensing (S0196-2892), 2010, 48(10): 3747-3762.
[20] Mura M D, Benediktsson J A, Waske B, et al.Extended Profiles with Morphological Attribute Filters for the Analysis of Hyperspectral Data[J]. International Journal of Remote Sensing (S0143-1161), 2010, 31(22): 5975-5991.
[21] Wang R, Nie F, Wang Z, et al.Multiple Features and Isolation Forest-Based Fast Anomaly Detector for Hyperspectral Imagery[J]. IEEE Transactions on Geoence and Remote Sensing (S0196-2892), 2020, 58(9): 1-13.