Sub-pixel spectral clustering model of quantum mechanism effect for hyperspectral images

The problem of pixel mixture, which is caused by low spatial resolution and complex land cover, restricts the development of target classification and detection for hyperspectral images (HSIs) in many application fields. In this paper, a novel sub-pixel spectral clustering algorithm of quantum mechanism effect is proposed for hyperspectral remote sensing images. In order to effectively describe the energy distribution of spectral vectors, we define a nonlinear mixture mathematic model with self-consistency for mixed pixel. Considering the self-consistent conditions between lattice models and quantum impurity model, Green function method is used to design the clusters mean field impurities solver, which can decompose the mixed spectral accurately and quickly. Finally, the spectral samples with smaller quantum potential energy and greater local density are applied as cluster center to obtain the final clustering result. The effectiveness of this method is illustrated on several hyperspectral remote sensing images, and experiments show that it can improve the ability of clustering high dimensional non-spherical structure data.