为了提高ResNet50网络对遥感场景图像中目标特征的提取能力和场景分类的可解释性,提出一种基于ResNet50-CBAM-FCAM(RCF)网络的遥感图像场景分类方法。该方法在ResNet50网络中增加卷积注意力模块和全卷积类激活映射分支,利用注意力机制将分支特征分别与提取的通道注意力特征和空间注意力特征融合,生成各类场景的类激活映射图。实验结果证明,所提方法在数据集AID上的总体分类准确率达到96%以上,在实验数据集NWPU-RESISC45上的总体分类准确率达到93%以上,且类激活映射图可视化结果可以准确聚焦遥感场景图像的目标对象。

To improve the ability of ResNet50 to extract target object features of remote sensing scene images and interpretability of scene classification, a Resnet50-CBAM-FCAM(RCF) network-based method of remote sensing image scene classification is proposed in this paper. This method increases the convolution attention module and full convolution-class activation mapping branch in the ResNet50 network. With the help of an attention mechanism, the branch features are fused with the extracted channel attention features and spatial attention features, respectively, and the class activation maps of various scenes are generated. The experimental results show that the overall classification accuracy of the proposed method in AID and NWPU-REISC45 datasets is more than 96% and 93%, respectively, and the visual results of the class activation maps can focus the target objects of remote sensing scene image accurately.