Local hyperspectral anomaly detection method based on low-rank and sparse matrix decomposition

Hongwei Chang; Tao Wang; Aihua Li; Hao Fang

doi:10.1117/1.JRS.13.026513

14 June 2019 Local hyperspectral anomaly detection method based on low-rank and sparse matrix decomposition

Hongwei Chang, Tao Wang, Aihua Li, Hao Fang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 13, Issue 2, 026513 (June 2019). https://doi.org/10.1117/1.JRS.13.026513

Abstract

Anomaly detection (AD) is an important technique for hyperspectral image processing and analysis. Typically, it is accomplished by extracting knowledge from the background and distinguishing anomalies and background using the difference between them. However, it is almost impossible to obtain “pure” background to achieve an ideal detection because of anomaly contamination. The low-rank and sparse matrix decomposition (LRaSMD) technique has been proved to have the potential to solve the aforementioned problem. But the accuracy and time consumption need to be further improved. Thus we propose a local hyperspectral AD method based on LRaSMD with an optimization algorithm for better performance. The LRaSMD technique is first implemented with semisoft Go decomposition (GoDec) rather than GoDec to quickly and accurately set the background apart from the anomalies. Then the low-rank prior knowledge of the background is fully explored to compute the background statistics. After that, the local Mahalanobis distance of pixels is calculated with the sliding dual-window strategy to detect the probable anomalies. The proposed method is validated using four real hyperspectral data sets with ground-truth information. Our experimental results indicate that the proposed method achieves better detection performance as compared with the comparison algorithms.

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Hongwei Chang, Tao Wang, Aihua Li, and Hao Fang "Local hyperspectral anomaly detection method based on low-rank and sparse matrix decomposition," Journal of Applied Remote Sensing 13(2), 026513 (14 June 2019). https://doi.org/10.1117/1.JRS.13.026513

Received: 26 December 2018; Accepted: 16 May 2019; Published: 14 June 2019

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