面向高光谱图像分类的多维协同与中心注意网络OA

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.