Regularized Multiple Sparse Bayesian Learning for Hyperspectral Target Detection

The sparse representation method has been successfully applied in the field of hyperspectral image target detection. It assumes that target detection can be achieved by using target and background libraries to represent test pixels. Under this formulation, the presentation of the target and background signatures can be solved by L-1-norm minimization of the weight coefficient and the target detection output is simply achieved by the difference between the two representation residuals. In this paper, a regularized multiple sparse Bayesian learning (RMSBL) method for hyperspectral target detection is proposed, which is established by Bayesian inference using the conditional posterior distributions of the model parameters under a hierarchical Bayesian model. According to the cost function for multiple sparse Bayesian learning, the presentation of the target and background signatures can be obtained by an L-2,L-1-norm iterative minimization method. And the target detection result can be achieved with the difference between the two representation residuals. Four groups of hyperspectral datasets are used for simulation experiments. The results are compared with those of other common detection algorithms. The experimental results demonstrate that the RMSBL algorithm has higher detection performance.