电讯技术2026，Vol.66Issue(5)：717-727,11.DOI:10.20079/j.issn.1001-893x.250106001
面向高光谱图像分类的多维协同与中心注意网络
Multi-dimensional Synergistic and Central Attention Network for Hyperspectral Image Classification
摘要
Abstract
At present,deep learning-based methods for hyperspectral image classification primarily rely on the rich spectral and spatial information contained in hyperspectral data.However,many existing approaches tend to overutilize either spatial or spectral information,thereby neglecting their potential correlations.To more effectively integrate spatial and spectral features,a multi-dimensional collaborative center attention(MCCA)network is proposed.First,a multi-dimensional collaborative attention module is designed to compute attention weights across channel,height,and width dimensions,enabling multi-dimensional feature extraction and the identification of key features.Second,a dynamic conditional position encoding module is introduced to dynamically adjust position encoding based on input,mitigating the interference caused by absolute position encodings in spatial feature extraction.Additionally,a spectral center attention module is developed to adaptively extract spatially relevant features of neighboring pixels by leveraging the spectral information of the central pixel,thereby enhancing the network's focus on central pixels.The proposed network achieves overall accuracies of 98.15%,99.09%,and 99.06%on the Indian Pines,Pavia University,and WHU-Hi-LongKou hyperspectral datasets,respectively,demonstrating robust classification performance even with relatively limited training samples.关键词
高光谱图像分类/多维协同/条件动态位置编码/光谱中心注意力Key words
hyperspectral image classification/multi-dimensional collaboration/dynamic conditional position encoding/spectral center attention分类
信息技术与安全科学引用本文复制引用
张丽丽,王春阳,刘佳辉,房启志..面向高光谱图像分类的多维协同与中心注意网络[J].电讯技术,2026,66(5):717-727,11.基金项目
辽宁省教育厅项目(JYTMS20230243) （JYTMS20230243）
