Intrinsic Decomposition based Tensor Modeling Scheme for Hyperspectral Target Detection

Motivated by its capacity to process complex characteristics and deal with nonlinear problems, tensor decompositions have been also introduced, recently, to treat remote sensing data. In this article, a new tensor formulation based feature extraction framework is suggested for hyperspectral target detection. The new proposed method includes the intrinsic decomposition, as tensor structures, to improve the hyperspectral data representation and get rid of the non-significant spatial proprieties. Besides of the joint exploitation of spectral and spatial content, the new proposed approach allows to extract more effective discriminative spatial features. A series of experiments, for the purpose of hyperspectral target detection, show that the suggested scheme can be conducted on hyperspectral images with satisfactory detection accuracies.