南京航空航天大学学报（英文版）2025，Vol.42Issue(z1)：102-120,19.DOI:10.16356/j.1005-1120.2025.S.009
基于谱段图卷积与注意力增强卷积联合网络的高光谱图像分类方法
A Hyperspectral Image Classification Based on Spectral Band Graph Convolutional and Attention-Enhanced CNN Joint Network
摘要
Abstract
Hyperspectral image(HSI)classification is crucial for numerous remote sensing applications.Traditional deep learning methods may miss pixel relationships and context,leading to inefficiencies.This paper introduces the spectral band graph convolutional and attention-enhanced CNN joint network(SGCCN),a novel approach that harnesses the power of spectral band graph convolutions for capturing long-range relationships,utilizes local perception of attention-enhanced multi-level convolutions for local spatial feature and employs a dynamic attention mechanism to enhance feature extraction.The SGCCN integrates spectral and spatial features through a self-attention fusion network,significantly improving classification accuracy and efficiency.The proposed method outperforms existing techniques,demonstrating its effectiveness in handling the challenges associated with HSI data.关键词
高光谱图像分类/谱段图卷积/注意力增强卷积/动态注意力/特征提取/特征融合Key words
hyperspectral classification/spectral band graph convolutional network/attention-enhance convolutional network/dynamic attention/feature extraction/feature fusion分类
信息技术与安全科学引用本文复制引用
徐陈捷,李丹,孔繁锵..基于谱段图卷积与注意力增强卷积联合网络的高光谱图像分类方法[J].南京航空航天大学学报（英文版）,2025,42(z1):102-120,19.基金项目
This work was supported in part by the National Natural Science Foundations of China(No.61801214),and the Postgraduate Research Practice Innova-tion Program of NUAA(No.xcxjh20231504). （No.61801214）
