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15 October 2015 Post-processing for improving hyperspectral anomaly detection accuracy
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Abstract
Anomaly detection is an important topic in the exploitation of hyperspectral data. Based on the Reed–Xiaoli (RX) detector and a morphology operator, this research proposes a novel technique for improving the accuracy of hyperspectral anomaly detection. Firstly, the RX-based detector is used to process a given input scene. Then, a post-processing scheme using morphology operator is employed to detect those pixels around high-scoring anomaly pixels. Tests were conducted using two real hyperspectral images with ground truth information and the results based on receiver operating characteristic curves, illustrated that the proposed method reduced the false alarm rates of the RXbased detector.
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Jee-Cheng Wu, Chi-Ming Jiang, and Chen-Liang Huang "Post-processing for improving hyperspectral anomaly detection accuracy", Proc. SPIE 9643, Image and Signal Processing for Remote Sensing XXI, 96430O (15 October 2015); doi: 10.1117/12.2193565; https://doi.org/10.1117/12.2193565
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