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Many target detectors commonly utilize a single a priori target spectral signature as an input. However, the detection results are greatly affected by the quality of the a priori target spectral signature because the spectral variability phenomenon is universal and anisotropic in hyperspectral image data. This paper proposes a sparse representation-based method to generate an optimized target spectrum from limited target training samples, which is able to alleviate the impact of spectral variability on hyperspectral target detection. When lacking comprehensive knowledge about the target object of interest, an optimized representative target spectrum should be expected to be reconstructed by the hyperspectral data themselves in a sparse representation manner following the characteristics of the data structure and then be generated by a set of selected candidate pixels that contain the target signal with a varying status. With the optimized a priori target signature, the experimental results of the detection of different characteristics of objects with three different types of hyperspectral images confirm the effectiveness, robustness, and generality performance of the proposed method.

A Sparse Representation Method for a Priori Target Signature Optimization in Hyperspectral Target Detection