 The proposed MAE net is a deep learning-based hyperspectral unmixing approach that addresses the spectral variability problem in real scenes by incorporating a multi-attention mechanism and a sparse constraint. It outperforms other state-of-the-art unmixing methods on both synthetic and real datasets. This article was authored by Li Juen-su, Jun Liu, Yan Yuan, and others.