SCVolFormer: Spectral Consistency–Guided Volumetric Linear Self–Attention Transformer for Hyperspectral Unmixing

Hyperspectral unmixing (HU) requires effective modeling of spectral–spatial information and local–global feature interactions to achieve accurate abundance estimation and endmember extraction. Although Transformer-based HU methods are effective in capturing long-range dependencies, they often neglect the intrinsic spectral consistency of hyperspectral data and do not fully exploit the global spectral–spatial correlations in hyperspectral image cubes. To address these issues, this paper proposes a Spectral Consistency–guided Transformer with Volumetric Linear Self-Attention (SCVolFormer) for hyperspectral unmixing. A Spectral Consistency Block (SCB) is introduced to preserve consistency across adjacent spectral bands and produce physically meaningful feature representations. A spectral grouping strategy is further adopted to partition the high-dimensional spectrum into locally continuous subspaces, reducing computational cost. In addition, a shared-weight Transformer encoder with Volumetric Linear Self-Attention (VolLSA) is designed to model interactions between the spectral and spatial dimensions and capture long-range dependencies within hyperspectral image cubes. A decoder is then used to estimate abundance maps and reconstruct hyperspectral images. Experiments on one synthetic dataset and three real hyperspectral datasets demonstrate that SCVolFormer outperforms state-of-the-art methods in abundance estimation and endmember extraction, confirming the effectiveness of spectral consistency guidance and volumetric attention modeling for hyperspectral unmixing.