Hyperspectral unmixing is a critical task in remote sensing, enabling the decomposition of hyperspectral data into their constituent endmembers and abundances. The loss of the traditional unmixing algorithm based on deep learning typically depends on reducing the discrepancy between the original and reconstructed hyperspectral image. However, during the training process, the loss feedback method is relatively simple, resulting highly random unmixing results. Moreover, spatial feature extraction can effectively improve the unmixing effect, but existing spatial feature extraction methods in hype...
