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Proceedings Paper

Post-processing for improving hyperspectral anomaly detection accuracy
Author(s): Jee-Cheng Wu; Chi-Ming Jiang; Chen-Liang Huang
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Paper 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.

Paper Details

Date Published: 15 October 2015
PDF: 6 pages
Proc. SPIE 9643, Image and Signal Processing for Remote Sensing XXI, 96430O (15 October 2015); doi: 10.1117/12.2193565
Show Author Affiliations
Jee-Cheng Wu, National Ilan Univ. (Taiwan)
Chi-Ming Jiang, National Ilan Univ. (Taiwan)
Chen-Liang Huang, National Ilan Univ. (Taiwan)

Published in SPIE Proceedings Vol. 9643:
Image and Signal Processing for Remote Sensing XXI
Lorenzo Bruzzone, Editor(s)

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