电讯技术2026，Vol.66Issue(2)：211-220,10.DOI:10.20079/j.issn.1001-893x.241031001
SpiralMamba:一种用于高光谱图像分类的轻量级Mamba网络
SpiralMamba:a Lightweight Mamba Network for Hyperspectral Image Classification
摘要
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,the authors propose SpiralMamba,a novel classification framework inspired by the recent Mamba model,renowned for its efficient global feature extraction with linear complexity.SpiralMamba comprises three main modules:Spiral Scanning Embedding(SSE)module minimizes the loss of spatial information when converting images into sequences;Gaussian Mask Weighting(GMW)module enhances the weight of features surrounding the central pixel,thereby improving the classifiability of the extracted features;Lightweight Mamba Module(LWM)reduces model parameters and computational demands,making this model suitable for hyperspectral image classification tasks with scarce samples.Experimental results on the Indian Pines,WHU-Hi-HanChuan and Houston2018 datasets demonstrate that the overall classification accuracies of the proposed SpiralMamba model reach 93.10%,93.49%,and 91.21%,respectively.关键词
高光谱图像分类/螺旋扫描嵌入(SSE)/高斯掩膜加权(GMW)/轻量级Mamba模块(LWM)Key words
hyperspectral image classification/spiral scan embedding(SSE)/Gaussian mask weighting(GMW)/lightweight Mamba module(LWM)分类
信息技术与安全科学引用本文复制引用
白玉,吴昊琦,张丽丽,国晗林..SpiralMamba:一种用于高光谱图像分类的轻量级Mamba网络[J].电讯技术,2026,66(2):211-220,10.基金项目
辽宁省教育厅项目(JYTMS20230243) （JYTMS20230243）
