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Proceedings Paper

Locality-constrained anomaly detection for hyperspectral imagery
Author(s): Jiabin Liu; Wei Li; Qian Du; Kui Liu
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Paper Abstract

Detecting a target with low-occurrence-probability from unknown background in a hyperspectral image, namely anomaly detection, is of practical significance. Reed-Xiaoli (RX) algorithm is considered as a classic anomaly detector, which calculates the Mahalanobis distance between local background and the pixel under test. Local RX, as an adaptive RX detector, employs a dual-window strategy to consider pixels within the frame between inner and outer windows as local background. However, the detector is sensitive if such a local region contains anomalous pixels (i.e., outliers). In this paper, a locality-constrained anomaly detector is proposed to remove outliers in the local background region before employing the RX algorithm. Specifically, a local linear representation is designed to exploit the internal relationship between linearly correlated pixels in the local background region and the pixel under test and its neighbors. Experimental results demonstrate that the proposed detector improves the original local RX algorithm.

Paper Details

Date Published: 9 December 2015
PDF: 13 pages
Proc. SPIE 9808, International Conference on Intelligent Earth Observing and Applications 2015, 980803 (9 December 2015); doi: 10.1117/12.2205326
Show Author Affiliations
Jiabin Liu, Beijing Univ. of Chemical Technology (China)
Wei Li, Beijing Univ. of Chemical Technology (China)
Qian Du, Mississippi State Univ. (United States)
Kui Liu, Intelligent Fusion Technology (United States)

Published in SPIE Proceedings Vol. 9808:
International Conference on Intelligent Earth Observing and Applications 2015
Guoqing Zhou; Chuanli Kang, Editor(s)

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