Efficient Blind Hyperspectral Unmixing with Non-Local Spatial Information Based on Swin Transformer

Blind hyperspectral unmixing (HU) involves identifying pixel spectra as distinct materials (endmembers) and simultaneously determining their proportions (abundances) at each pixel. In this paper, we present Swin-HU, a novel method based on the Swin Transformer, designed to efficiently tackle blind HU. This method addresses the limitations of existing techniques, such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViT), in capturing global spatial information and spectral sequence attributes. Swin-HU employs Window Multi-head Self-Attention (W-MSA) and Shifted Window Multi-head Self-Attention (SW-MSA) mechanisms to extract global spatial priors while maintaining linear computational complexity. We evaluate Swin-HU against six other unmixing methods on both synthetic and real datasets, demonstrating its superior performance in endmember extraction and abundance estimation. The source code is available at https://github.com/wangyunjeff/Swin-HU.