Locality-constrained anomaly detection for hyperspectral imagery

Jiabin Liu; Wei Li; Qian Du; Kui Liu

doi:10.1117/12.2205326

9 December 2015 Locality-constrained anomaly detection for hyperspectral imagery

Jiabin Liu, Wei Li, Qian Du, Kui Liu

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.

Citation Download Citation

Jiabin Liu, Wei Li, Qian Du, and 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); https://doi.org/10.1117/12.2205326

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KEYWORDS

Sensors

Detection and tracking algorithms

Hyperspectral imaging

Hyperspectral target detection

Target detection

Mahalanobis distance

Statistical analysis

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