Hyperspectral target detection based on kernel sparse and spatial constraint

This paper proposes a target detector based on kernel sparse and spatial constraint for hyperspectral imagery (HSI). Due to the nonlinear and structural features of HSI data, sparse representation and spatial constraint are taken into consideration. Firstly, we construct a dictionary to represent the target pixels within a small neighborhood by a linear combination of samples. Then, these targets pixels are projected into the high-dimensional feature space through kernel function and orthogonal matching pursuit (OMP) are kernelized to obtain recovered sparse coefficient vector. By comparing the residuals of background and target to determine the type of pixel, the preliminary detection result can be achieved. Lastly, a spatial over-complete basis matrix is used to revise the initial detection result. The experimental results show that the proposed detector has better detection performance than several typical detectors.