Search

Space target recognition is of great importance for maintaining aerospace safety and national security. When observing a space target, owing to the low spatial resolution of ground-based observation equipment, each pixel in a hyperspectral image might represent a mixture of several different materials. Hyperspectral unmixing is a process used to extract the endmembers and their corresponding abundances from hyperspectral data. Unfortunately, most existing methods cannot make full use of the available spatial information data. The paper proposes a new local manifold sparse regularized unmixing model based on similarity regularized nonnegative matrix factorization (SRNMF). To exploit the spatial information of the vis-NIR (approximately 400-2500 nm) hyperspectral image of a space target, image segmentation is introduced to generate similar local regions. These local regions are generated adaptively, and pixels within each region have similar abundance sparseness. Simulation experiments validated the high efficiency and precision of the proposed algorithm, which should also be suitable for other spectral analysis applications. [ABSTRACT FROM AUTHOR]