S3ViT: self-supervised spectral vision transformer framework for hyperspectral unmixing

Hyperspectral unmixing aims to decompose each pixel in a hyperspectral image into a set of constituent endmembers and their corresponding abundances. Recent deep learning based approaches have demonstrated strong performance in capturing both spectral and spatial features. However, obtaining reliable per-pixel abundance ground truth in real hyperspectral scenes is generally infeasible, which motivates unsupervised and self-supervised unmixing strategies. In this work, we propose S3ViT, a self-su