Enhanced Spatial-Spectral Attention Network for Hyperspectral Image Unmixing

Deep learning has shown great promise in hyperspectral unmixing (HU), especially the unmixing methods based on autoencoder (AE) networks, which are the most prevalent these days. Since most spectral mixing problems are nonlinear and cannot effectively utilize global context information, these methods have limited generalization ability under different ground features and scenarios. To address these limitations, an enhanced network based on spatial-spectral information attention is proposed. A two-channel attention mechanism is embedded within the convolutional AE to acquire the feature dependencies. The spatial information extraction uses the dynamic large kernel block (DLK) to obtain the global spatial attention of the image. The DLK module uses multiple large kernels with different kernel sizes and dilation rates to capture multiscale features, and the spectral information extraction uses coordinate attention (CA) to capture spectral correlation information. This can improve the quality of the endmember spectra and abundance maps. On real and synthetic data, this model is compared with several advanced unmixing methods, and the results show the effectiveness of this method.