Post-processing for improving hyperspectral anomaly detection accuracy

Jee-Cheng Wu; Chi-Ming Jiang; Chen-Liang Huang

doi:10.1117/12.2193565

15 October 2015 Post-processing for improving hyperspectral anomaly detection accuracy

Jee-Cheng Wu, Chi-Ming Jiang, Chen-Liang Huang

Proceedings Volume 9643, Image and Signal Processing for Remote Sensing XXI; 96430O (2015) https://doi.org/10.1117/12.2193565
Event: SPIE Remote Sensing, 2015, Toulouse, France

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.

Citation Download Citation

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); https://doi.org/10.1117/12.2193565

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KEYWORDS

Sensors

Hyperspectral imaging

Binary data

Image processing

Receivers

Target detection

Hyperspectral target detection

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