A Lightweight State Space Model With Multiscale Morphology and Low-Rank Head for Hyperspectral Image Classification

State space models (SSMs) have advanced hyperspectral image (HSI) classification, yet existing approaches have limitations. They typically rely on single-scale feature extractors, limiting their ability to model various spatial geometries, and their standard backbones can exhibit training instability when processing complex HSI data. To overcome these challenges, this paper proposes a novel lightweight multiscale morphology-enhanced low-rank head residual state space network (MMLH-RSSN), built on a synergistic framework developed for robust feature representation and efficient modeling. Specifically, we first designed a multiscale morphological module to explicitly capture hierarchical spatial features, a crucial step for distinguishing spectrally similar classes with varying scales. To effectively encode these complex features, we then introduced an enhanced Residual SSM, which integrates residual connections and layer normalization to significantly improve model stability and learning capacity. An end-to-end lightweight design was ensured by a parameter-efficient low-rank decomposition head. Extensive experiments on four benchmark datasets show that MMLH-RSSN achieves state-of-the-art performance, with overall accuracies of 98.51% and 99.69% on the Pavia University and Botswana datasets, using only 0.063 M parameters. This work demonstrates that a synergistic combination of multiscale priors and a stabilized SSM backbone offers a highly accurate and efficient solution for HSI classification, particularly for resource-constrained scenarios.