A Spiking and Memory-Enhanced State-Space Model for Hyperspectral Image Classification

Hyperspectral imaging (HSI) captures hundreds of contiguous spectral bands and enables fine-grained material discrimination. We present SpikeHopMamba, a hybrid architecture that combines neuromorphic spike encoding, a linearized Hopfield energy module, and selective state-space (SSSM) blocks to produce compact, multi-modal patch tokens for HSI classification. SpikeHopMamba fuses static spectral tokens, sequentially encoded spiking features, and patch-level associative-energy cues, then models long-range spatial dependencies via efficient SSSM blocks. We train the model end-to-end with cross-entropy (and label smoothing) and report results averaged over five independent runs. Experiments on Hanchuan (HC), Salinas (SA), and OHID1–9 show that SpikeHopMamba attains competitive accuracy while preserving computational efficiency (OA up to 99.60%, κ up to 99.53%).