Lightweight Mamba Model Based on Spiral Scanning Mechanism for Hyperspectral Image Classification

Abstract

Hyperspectral image classification (HSIC) has advanced significantly in recent years, driven by the development of advanced algorithms in remote sensing. However, the high-dimensional nature of hyperspectral data and the limited availability of labeled samples remain significant challenges, hindering the effectiveness of many existing methods. To address these limitations, we propose SpiralMamba, a novel classification framework inspired by the recent Mamba model, renowned for its efficient global feature extraction with linear complexity. To minimize the loss of spatial information when converting images into sequences for Mamba processing, we propose the innovative spiral scan embedding (SSE) module. In addition, the introduction of the Gaussian mask weighting (GMW) module enhances the feature weights around the central pixel, thereby improving the classifiability of the extracted features. We introduce the lightweight Mamba module (LWM), which reduces model parameters and computational requirements, making it particularly well-suited for HSIC with limited samples. Experimental results on three real datasets demonstrate that the SpiralMamba model outperforms existing methods in various performance metrics.