2023
DOI: 10.36227/techrxiv.23815902.v1
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Contrastive Learning approach for blind Hyperspectral Unmixing (CLHU)

Abstract: <p>The Contrastive Learning for blind Hyperspectral Unmixing (CLHU) is a self-supervised deep learning approach for blind hyperspectral unmixing. Unlike existing deep learning methods that rely on reconstruction capabilities, CLHU leverages the input-endmembers relationship for abundance estimation. The endmembers are dynamically updated during training, controlled by two regularization factors. Extensive experiments demonstrate CLHU's effectiveness, achieving state-of-the-art performance in hyperspectra… Show more

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