  Hyperspectral unmixing is a critical yet challenging task in hyperspectral
image interpretation. Recently, great efforts have been made to solve the
hyperspectral unmixing task via deep autoencoders. However, existing networks
mainly focus on extracting spectral features from mixed pixels, and the
employment of spatial feature prior knowledge is still insufficient. To this
end, we put forward a spatial attention weighted unmixing network, dubbed as
SAWU-Net, which learns a spatial attention network and a weighted unmixing
network in an end-to-end manner for better spatial feature exploitation. In
particular, we design a spatial attention module, which consists of a pixel
attention block and a window attention block to efficiently model pixel-based
spectral information and patch-based spatial information, respectively. While
in the weighted unmixing framework, the central pixel abundance is dynamically
weighted by the coarse-grained abundances of surrounding pixels. In addition,
SAWU-Net generates dynamically adaptive spatial weights through the spatial
attention mechanism, so as to dynamically integrate surrounding pixels more
effectively. Experimental results on real and synthetic datasets demonstrate
the better accuracy and superiority of SAWU-Net, which reflects the
effectiveness of the proposed spatial attention mechanism.
