GAFnet: Group Attention Fusion Network for PAN and MS Image High-Resolution Classification
提出一种深度组空间-光谱注意力融合网络，通过组空间注意力和组光谱注意力模块提取特征并融合，用于全色和多光谱图像的高分辨率分类，在四个数据集上取得良好效果。
Panchromatic (PAN) and multispectral (MS) images have coordinated and paired spatial spectral information, which can complement each other and make up for their shortcomings for image interpretation. In this article, a novel classification method called the deep group spatial-spectral attention fusion network is proposed for PAN and MS images. First, the MS image is processed by unpooling to obtain the same resolution as that of the PAN image. Second, the group spatial attention and group spectral attention modules are proposed to extract image features. The PAN and the processed MS images are regarded as the input of the two modules, respectively. Third, the features from the previous step are fused by the attention fusion module, which aims to fully fuse multilevel features, take into account both the low-level features and the high-level features, and maintain the global abstract and local detailed information of the pixels. Finally, the fusion feature is fed into the classifier and the resulting map is obtained by pixel level. Extensive experiments and analysis on four datasets show that the proposed method achieves comparable results.