Spatial feature extraction non-negative tensor factorization for hyperspectral unmixing

Estimating endmembers and corresponding abundances from mixed pixels are essential steps for hyperspectral unmixing. In hyperspectral unmixing, obtaining accurate unmixing results is difficult since less prior knowledge is available. Besides, the unmixing results are influenced by noise and highly correlated endmembers, so that the obtained abundance maps exist small values which are not present in the image. In this paper, we separate each...