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

Authors

Liu Xuan, Rocket Force University of Engineering, Xi'an 710025, China;
Xiangyang Li, Rocket Force University of Engineering, Xi'an 710025, China;
He Fang, Rocket Force University of Engineering, Xi'an 710025, China;
Jianwei Zhao, Rocket Force University of Engineering, Xi'an 710025, China;
Fenggan Zhang, Rocket Force University of Engineering, Xi'an 710025, China;

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.

Recommended Citation

Xuan, Liu; Li, Xiangyang; Fang, He; Zhao, Jianwei; and Zhang, Fenggan (2022) "Hyperspectral RX Anomaly Detection Method Based on the Fusion of Spatial and Spectral Feature," Journal of System Simulation: Vol. 33: Iss. 12, Article 13.
DOI: 10.16182/j.issn1004731x.joss.21-FZ0780
Available at: https://dc-china-simulation.researchcommons.org/journal/vol33/iss12/13

First Page

2891

Revised Date

2021-07-30

DOI Link

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

Last Page

2900

CLC

TP795;TP391.9

Recommended Citation

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.

DOI

10.16182/j.issn1004731x.joss.21-FZ0780