A Nonlinear Sparse Representation-Based Binary Hypothesis Model for Hyperspectral Target Detection

The sparsity model has been employed for hyperspectral target detection and has been proved to be very effective when compared to the traditional linear mixture model. However, the state-of-art sparsity models usually represent a test sample via a sparse linear combination of both target and background training samples, which does not result in an efficient representation of a background test sample. In this paper, a sparse representation-based binary hypothesis (SRBBH) model employs more appropriate dictionaries with the binary hypothesis model to sparsely represent the test sample. Furthermore, the nonlinear issue is addressed in this paper, and a kernel method is employed to resolve the detection issue in complicated hyperspectral images. In this way, the kernel SRBBH model not only takes the nonlinear endmember mixture into consideration, but also fully exploits the sparsity model by the use of more reasonable dictionaries. The recovery process leads to a competition between the background and target subspaces, which is effective in separating the targets from the background, thereby enhancing the detection performance.