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Open AccessArticle

Hyperspectral Anomaly Detection via Dictionary Construction-Based Low-Rank Representation and Adaptive Weighting

School of Physics and Optoelectronic Engineering, Xidian University, Xi’an 710071, China
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Remote Sens. 2019, 11(2), 192; https://doi.org/10.3390/rs11020192
Received: 13 December 2018 / Revised: 16 January 2019 / Accepted: 17 January 2019 / Published: 19 January 2019
(This article belongs to the Special Issue Remote Sensing for Target Object Detection and Identification)
Anomaly detection (AD), which aims to distinguish targets with significant spectral differences from the background, has become an important topic in hyperspectral imagery (HSI) processing. In this paper, a novel anomaly detection algorithm via dictionary construction-based low-rank representation (LRR) and adaptive weighting is proposed. This algorithm has three main advantages. First, based on the consistency with AD problem, the LRR is employed to mine the lowest-rank representation of hyperspectral data by imposing a low-rank constraint on the representation coefficients. Sparse component contains most of the anomaly information and can be used for anomaly detection. Second, to better separate the sparse anomalies from the background component, a background dictionary construction strategy based on the usage frequency of the dictionary atoms for HSI reconstruction is proposed. The constructed dictionary excludes possible anomalies and contains all background categories, thus spanning a more reasonable background space. Finally, to further enhance the response difference between the background pixels and anomalies, the response output obtained by LRR is multiplied by an adaptive weighting matrix. Therefore, the anomaly pixels are more easily distinguished from the background. Experiments on synthetic and real-world hyperspectral datasets demonstrate the superiority of our proposed method over other AD detectors. View Full-Text
Keywords: anomaly detection; hyperspectral imagery; low-rank representation; dictionary construction; HSI reconstruction; sparse coding; adaptive weighting anomaly detection; hyperspectral imagery; low-rank representation; dictionary construction; HSI reconstruction; sparse coding; adaptive weighting
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Yang, Y.; Zhang, J.; Song, S.; Liu, D. Hyperspectral Anomaly Detection via Dictionary Construction-Based Low-Rank Representation and Adaptive Weighting. Remote Sens. 2019, 11, 192.

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