计算机技术与发展2024，Vol.34Issue(10)：53-60,8.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0189
基于多重注意力与混合残差卷积的高光谱地物分类
High-spectral-resolution Hyperspectral Land Cover Classification Based on Multi-head attention and Hybrid Residual Convolution
摘要
Abstract
To address common challenges in hyperspectral datasets such as small sample sizes,high dimensionality,high spectral correlation between bands,and the inability to perform deep-level data mining on images,we propose a high-spectral-resolution hyperspectral land cover classification based on multi-head attention and hybrid residual convolution networks(RCANN-Net).Firstly,principal component analysis(PCA)is employed to reduce the dimensionality of hyperspectral images,and multi-scale 3D convolutional operations are performed to extract multi-scale feature information.Subsequently,this feature information is input into an improved 3D residual spatial-channel attention module,which not only learns features but also transmits parameters and corrects the weights of feature layers,resulting in joint fine-grained spectral-spatial features of hyperspectral images.Simultaneously,parallel deep separable convolutional residual spatial attention modules are introduced to bias the model towards learning spatial features of hyperspectral images.Finally,the classification results are obtained through the result prediction module based on the feature information.Through multiple comparisons on three publicly available hyperspectral datasets,the proposed method outperforms four other comparative methods in terms of overall accuracy(OA),average accuracy(AA),KAPPA coefficient,and average training time.关键词
高光谱图像分类/深度学习/特征融合/深度可分离卷积/注意力机制/残差网络Key words
hyperspectral image classification/deep learning/feature fusion/deep separable convolution/attention mechanism/residual network分类
信息技术与安全科学引用本文复制引用
彭逸清,闫晓奇,任小玲..基于多重注意力与混合残差卷积的高光谱地物分类[J].计算机技术与发展,2024,34(10):53-60,8.基金项目
国家自然科学基金面上项目(61971339) （61971339）
陕西省自然科学基础研究计划重点项目(2018JZ6002) （2018JZ6002）
西安工程大学研究生创新基金项目(chx2023022) （chx2023022）
