自然资源遥感2025，Vol.37Issue(5)：101-112,12.DOI:10.6046/zrzyyg.2024191
改进的残差式3D-CNN和近邻注意力的高光谱遥感图像分类
Hyperspectral remote sensing image classification using improved residual 3D-CNN and neighborhood attention
摘要
Abstract
Hyperspectral remote sensing image classification has attracted widespread attention,yet the performance of classification methods remains greatly limited by challenges such as spectral variability(same object with different spectra),spectral confusion(different objects with similar spectra),and limited availability of training samples.To fully exploit the spatial-spectral features of hyperspectral images,this study proposed an improved network integrating residual convolution and neighborhood attention mechanisms.The proposed method consists of:(1)a residual-based spectral feature extraction module combining residual connections and a 3D convolutional neural network(3D-CNN);(2)a spatial-spectral feature fusion module using mixed convolutions;and(3)a neighborhood attention module designed to enhance the model's ability to focus on homogeneous regions.Experiments were conducted on three public hyperspectral datasets-Indian Pines,Pavia University,and Houston 2013.The results demonstrate that the proposed method achieves higher classification accuracy compared to recent state-of-the-art approaches.Using less than 10%of the samples for training,it attains overall accuracies of 99.39%,99.67%,and 98.64%,respectively,confirming its capability for high-accuracy classification under small-sample conditions.关键词
高光谱图像分类/卷积神经网络/残差连接/近邻注意力Key words
hyperspectral image classification/convolutional neural network/residual connection/neighborhood at-tention分类
计算机与自动化引用本文复制引用
潘增滢,吴瑞姣,林易丰,翁谦,林嘉雯..改进的残差式3D-CNN和近邻注意力的高光谱遥感图像分类[J].自然资源遥感,2025,37(5):101-112,12.基金项目
福建省自然科学基金项目"人机协同的自然资源要素提取关键技术研究"(编号:2023J01432)和国家自然科学基金项目"基于深度迁移学习网络的高分影像土地利用分类方法研究"(编号:41801324)共同资助. （编号:2023J01432）
