Spatial-Spectral Attention Bilateral Network for Hyperspectral Unmixing

Autoencoders are widely utilized in hyperspectral unmixing as an unsupervised end-to-end learning model. In particular, convolutional autoencoder networks are popular for processing multidimensional hyperspectral features. Nonetheless, the traditional convolutional Autoencoder network’s receptive field is constrained in the unmixing task, and establishing the connection between the local spatial neighborhood and the local spectrum fails to improve unmixing performance significantly. To address these limitations, a bilateral global attention network based on both spatial and spectral information is proposed. It enables the network to obtain respective feature dependencies in the two dimensions and achieve optimal fusion of both features. The network comprises two information extraction branches. The spatial information extraction branch uses the Swin Transformer block to acquire the global spatial attention of the overall image, while the spectral information extraction branch designates a simplified spectral channel attention mechanism to gain spectral attention weight maps. The network’s efficacy is demonstrated through a comparative study using a synthetic dataset and two real datasets. The code of this work is available at https://github.com/UPCGIT/SSABN.