Kernel-based invariant subspace method for hyperspectral target detection

In this paper, a kernel-based invariant subspace detection method is proposed for small target detection of hyperspectral images. The method combines kernel principal component analysis (KPCA) and the linear mixture model (LMM). The LMM is used to describe each pixel in the hyper-spectral image as a mixture of target, background and noise. The KPCA is used to build subspaces of the target and background. A generalized likelihood ratio test is used to detect whether each pixel in the hyperspectral image includes the target. Numerical experiments are performed on AVIRIS hyperspectral data with 126 bands. The experimental results show the effectiveness of the proposed method and prove that this method can commendably overcome spectral variability in hyperspectral target detection, and it has good ability to separate target from background.