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TGRS
2016

Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation

3 years 1 months ago
Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation
Abstract—A novel method for anomaly detection in hyperspectral images (HSIs) is proposed based on low-rank and sparse representation. The proposed method is based on the separation of the background and the anomalies in the observed data. Since each pixel in the background can be approximately represented by a background dictionary and the representation coefficients of all pixels form a low-rank matrix, a low-rank representation is used to model the background part. To better characterize each pixel’s local representation, a sparsity-inducing regularization term is added to the representation coefficients. Moreover, a dictionary construction strategy is adopted to make the dictionary more stable and discriminative. Then, the anomalies are determined by the response of the residual matrix. An important advantage of the proposed algorithm is that it combines the global and local structure in the HSI. Experimental results have been conducted using both simulated and real data sets....
Yang Xu, Zebin Wu, Jun Li, Antonio J. Plaza, Zhihu
Added 10 Apr 2016
Updated 10 Apr 2016
Type Journal
Year 2016
Where TGRS
Authors Yang Xu, Zebin Wu, Jun Li, Antonio J. Plaza, Zhihui Wei
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