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.

提出了一种基于空间注意力权重的混合网络（SAWU-Net），该网络可以更好地在端到端的模式下，学习空间注意力网络和加权混合网络，以更好地利用空间特征。 SAWU-Net通过像素注意块和窗口注意块有效地建模基于像素的光谱信息和基于补丁的空间信息，生成动态自适应的空间权重，以更有效地集成周围像素，其实验结果表明SAWU-Net具有更好的精度和优越性，反映了所提出的空间注意力机制的有效性。

SAWU-Net：面向高光谱图像的空间注意力加权解混合网络