Gated Spiking Attention Transformer for Hyperspectral Image Classification

Deploying deep learning models for hyperspectral image classification (HSIC) on resource-constrained edge or non-orbit platforms is challenging due to high energy consumption and computational cost. To address this, this paper proposes a lightweight spike-driven Vision Transformer, termed ‘GS-SpikeFormer’, leveraging energy-efficient spiking neural networks (SNNs). Our work makes three key contributions. First, we introduce a Gated Spiking Attention (GSA) mechanism that replaces costly matrix multiplication with binary event-driven feature gating using only logical AND operations. Second, we incorporate groupwise convolution into both self-attention and feedforward modules, reducing trainable parameters to just 0.35 M while preserving local spectral-spatial feature extraction. Third, extensive experiments on four benchmark hyperspectral datasets under limited training sample show that GS-SpikeFormer achieves competitive results with only 4 time steps (e.g., 97.54% on Indian Pines, 98.62% on Pavia University, 98.89% on Salinas, and 98.39% on Houston2013). Compared to prior SNN-based HSIC methods, our model achieves superior parameter and energy efficiency.