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An Automatic Dictionary Construction Framework for Sparsity-Based Hyperspectral Target Detectors

The mechanisms behind the sparsity-based techniques for hyperspectral target detection and classifications applications are quite similar except the construction methods of the dictionaries used by the algorithms. In hyperspectral image classification, the dictionaries are formed using some known labeled training samples for each class. In contrast, the only a priori target spectrum information is available for the target detection applications. In addition, most of the time, the background materials are unknown in an arbitrary scene. Although some practical approaches such as sliding window exist, their performances are highly unsatisfactory. In order to increase the detection performance of the sparsity-based methods, we propose an automatic dictionary construction framework which is based on a couple of stages consisting of dimension reduction, k-means clustering, connected component labeling via spatial techniques, and spectral methods such as constrained energy minimization filtering. Our experiments show that the proposed approach outperforms the conventional methods, and it is a promising framework for hyperspectral target detection applications.