PHDMamba: Progressive Hybrid Mamba for Hyperspectral Image Classification

Yichu Xu, Chengxi Han, Shi Chen, Yao Jin, Yuchun Miao, Haonan Guo, Di Wang

Abstract: Although Mamba-based models have demonstrated great potential in hyperspectral image (HSI) classification, existing approaches often rely on patch-wise inputs, causing redundant computation and limiting global spectral–spatial modeling, while the absence of hierarchical representation and explicit interaction further constrains fine-grained fusion. To address these limitations, we propose a Progressive Hybrid Mamba Model (PHDMamba), which is designed to progressively capture long-range spectral-spatial contextual dependencies and gradually fuse spectral and spatial information through adaptive feature interaction, ultimately yielding a unified representation. Specifically, we develop a Progressive Hybrid Mamba Module, which performs stage-wise modeling of long-range dependencies along both spectral and spatial dimensions. In addition, a dedicated Spectral-Spatial Interaction Module is introduced to adaptively integrate contextual spectral and spatial features. Experimental results on three widely used HSI benchmark datasets demonstrate that the proposed method achieves superior classification performance compared to existing approaches. The implementation code will be released publicly.

External IDs:doi:10.1109/lgrs.2025.3626712