Hyperspectral unmixing (HU) is a fundamental and critical task in various hyperspectral image (HSI) applications. Over the past few years, the linear mixing model (LMM) has received widely attention for its high efficiency, definite physical meaning, and being amenable to mathematical treatment. Among the various linear unmixing methods, the autoencoder unmixing network has achieved superior performance and presented more significant potential because of the powerful data fitting ability and deep feature acquisition. However, the autoencoder un...
