9 December 2015 Locality-constrained anomaly detection for hyperspectral imagery
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Proceedings Volume 9808, International Conference on Intelligent Earth Observing and Applications 2015; 980803 (2015) https://doi.org/10.1117/12.2205326
Event: International Conference on Intelligent Earth Observing and Applications, 2015, Guilin, China
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.
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Jiabin Liu, Jiabin Liu, Wei Li, Wei Li, Qian Du, Qian Du, Kui Liu, Kui Liu, } "Locality-constrained anomaly detection for hyperspectral imagery", Proc. SPIE 9808, International Conference on Intelligent Earth Observing and Applications 2015, 980803 (9 December 2015); doi: 10.1117/12.2205326; https://doi.org/10.1117/12.2205326
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