A Kernel Background Purification Based Anomaly Target Detection Algorithm for Hyperspectral Imagery

In traditional anomaly detection algorithms, the background information is approximately described by whole hyperspectral imagery. However, the disparity between true and estimated background information would influence the performance of detection algorithms using background information. Considering this problem, a kernel background purification based anomaly target detection method is proposed in this paper. The main idea of the proposed method is to estimate background information more accurately. It contains two main steps: Firstly, the pure background pixel set extraction using the kernel-based method. Secondly, background covariance matrix estimation by extracted pure background pixel set. Experiments implemented on San Diego and PHI data indicate that the proposed method performed better than global Reed-Xiaoli detector (RXD), kernel RXD, and collaborative representation detector (CRD).

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