液晶与显示2026，Vol.41Issue(7)：995-1008,14.DOI:10.37188/CJLCD.2026-0034
高低频增强与跨层图卷积聚合的高光谱图像分类
Hyperspectral image classification via high-low frequency enhancement and cross-layer graph convolution aggregation
摘要
Abstract
To address the problems of local texture and edge-detail loss,limited receptive fields in the convolution branch,and insufficient use of cross-layer structural information in the graph branch for hyperspectral image classification,this paper proposes a CNN-GCN joint classification model with high-low frequency enhancement and cross-layer graph convolution aggregation.The model improves input representation through high-low frequency residual enhancement,extracts multi-scale spectral-spatial features using a multi-stage dynamic convolution encoder,enhances regional structural modeling by cross-layer weighted aggregation in the graph branch,and performs collaborative modeling through cross-branch attention fusion.Experiments on three public datasets,Indian Pines,Pavia University,and Salinas,achieve overall accuracies of 92.94%,95.11%,and 97.50%,with corresponding Kappa coefficients of 91.94%,93.50%,and 97.22%,respectively.The results show that the proposed method can effectively integrate local details,spatial context,and regional topological structure information,achieving competitive classification performance across different types of hyperspectral classification scenes.关键词
高光谱图像分类/空间-光谱联合特征/图卷积网络/注意力融合Key words
hyperspectral image classification/spectral-spatial joint features/graph convolution network/attention fusion分类
信息技术与安全科学引用本文复制引用
马鑫,汪西原,白雪冰..高低频增强与跨层图卷积聚合的高光谱图像分类[J].液晶与显示,2026,41(7):995-1008,14.基金项目
国家自然科学基金(No.42361056)Supported by National Natural Science Foundation of China(No.42361056) （No.42361056）
