COLLABORATIVE-COMPETITIVE REPRESENTATION WITH SPATIAL REGULARIZATION FOR HYPERSPECTRAL ANOMALY DETECTION

Recently, Collaborative representation (CR) has drawn much attention towards anomaly detection for hyperspectral imagery. Pixels in the background can be represented with spatial neighbors. In CR, an l
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norm is implemented for estimating the weight vector in a closed-form solution. In this paper, locality constraints are imposed on the representation framework called collaborative-competitive representation for hyperspectral anomaly detection (CCRD). By incorporating competition among neighboring atoms lying in various subspaces, local information can be incorporated into the global framework of CR. In addition, distance weighted Tikhonov regularization is used to enhance the performance of CCRD, named CCRDT. Moreover, spatial information is used in the objective function of CCRDT, resulting in SCCRDT, to further enhance the performance of anomaly detection. Experimental results on several hyperspectral datasets demonstrate the superiority of proposed methods in comparison to traditional detectors, such as Reed-Xiaoli (RX) algorithm, kernel RX (KRX) algorithm, and existing collaborative representation-based anomaly detectors.