Contrastive Learning approach for blind Hyperspectral Unmixing (CLHU)

The Contrastive Learning for blind Hyperspectral Unmixing (CLHU) is a self-supervised deep learning approach for blind hyperspectral unmixing by jointly estimating the endmembers and fractional abundances. Unlike existing deep learning methods that rely on reconstruction capabilities, CLHU leverages the input-endmembers relationship for abundance estimation. Extensive experiments demonstrate CLHU’s effectiveness, achieving state-of-the-art performance in hyperspectral unmixing. Furthermore, the experimental findings indicate that endmember interactions are considered during the estimation process by the proposed method, particularly in non-linear problem contexts. This novel approach offers a promising perspective for the field and holds potential for further enhancements in hyperspectral unmixing tasks.