SSH-Net: Symplectic-Inspired Discrete Dynamic Evolution for Hyperspectral Image Classification

Abstract

Hyperspectral image classification (HSIC) relies on effective modeling of coupled spectral–spatial interactions. While recent CNN-, Transformer-, and Mamba-based methods have improved feature extraction, most of them organize token interaction through layerwise aggregation and repeated stacking, leaving cross-layer propagation only implicitly modeled. To address this issue, we propose SSH-Net, a symplectic-inspired spectral–spatial dynamic interaction network for HSIC. The proposed framework parameterizes token representations as latent position and momentum states and performs gated leapfrog-style dynamic updates to realize structured multistep feature propagation. On top of this dynamic backbone, a terminal-state descriptor branch summarizes the final position, momentum, and interaction-response statistics as terminal-state evidence, and a discriminative evidence aggregation (DEA) branch integrates such evidence with multiscale spatial and global contextual features for prediction. In this way, SSH-Net combines Hamiltonian-inspired dynamic propagation with complementary state-aware readout for hyperspectral classification. Experiments on four public benchmarks, namely Indian Pines, Houston2013, WHU-Hi-LongKou, and WHU-Hi-HanChuan, show that SSH-Net achieves consistently competitive or superior performance in terms of overall accuracy (OA), average accuracy (AA), and kappa coefficient compared with representative baseline methods. These results suggest that symplectic-inspired dynamic propagation provides a useful inductive bias for organizing spectral–spatial interaction in HSIC. Codes are available at https://github.com/yi1275174812/SSH-Net