SISTOR: A statistics-inspired sparsity target detector for hyperspectral images

Sparse representation has achieved great success in the hyperspectral image processing field. However, with regard to target detection, the state-of-the-art sparsity-based algorithms are ad hoc and no different to a classifier. In this paper, a novel target detection algorithm is proposed, combining an elaborately designed sparsity model and the binary hypothesis statistics. With the strong similarity of the material spectra from the same class, sparse representation theory is explored by constructing hypothesis-designed dictionaries. Based on the local smooth property, locally optimized selection methods are employed for the background samples. For hyperspectral images, the pixels are usually assumed to obey a Gaussian normal distribution. Therefore, in this paper, a statistics-inspired sparsity model is established. The generalized likelihood ratio test is utilized to solve the model and build a statistics-inspired sparsity target detector (SISTOR). A number of experiments were conducted to illustrate the performance of the proposed algorithm.