机电工程技术2026，Vol.55Issue(17)：26-33,8.DOI:10.3969/j.issn.1009-9492.2026.17.005
基于混合空洞卷积的高光谱图像分类方法
A Hyperspectral Image Classification Method Based on Hybrid Dilated Convolution
摘要
Abstract
Hyperspectral image classification is often hindered by high-dimensional redundancy and complex spatial structures,leaving room for improvement in both accuracy and efficiency.To address the issue of insufficient receptive fields in the convolutional branches of convolutional neural network-graph convolutional network(CNN-GCN)fusion frameworks,an improved method based on hybrid dilated convolution,termed DC-AMGCFN,is proposed.This method introduces dilated convolutions in the final layers of the large-and medium-scale branches to expand the receptive field,and integrates multi-hop graph convolutions with cross-branch attention mechanisms to achieve spectral-spatial feature fusion.Experimental results on three benchmark datasets:Indian Pines,Pavia University,and Salinas,demonstrate that the overall classification accuracies reach 92.70%,95.18%,and 97.30%,respectively.Ablation studies confirm that a moderate dilation configuration significantly optimizes feature extraction.The research indicates that hybrid dilated convolution can achieve a favorable balance between accuracy and efficiency,providing a lightweight,effective,and reusable improvement strategy for CNN-GCN fusion.关键词
高光谱图像分类/空洞卷积/感受野/卷积-图卷积融合Key words
hyperspectral image classification/dilated convolution/receptive field/CNN-GCN 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..基于混合空洞卷积的高光谱图像分类方法[J].机电工程技术,2026,55(17):26-33,8.基金项目
国家自然科学基金(42361056) （42361056）
