A Multi-Scale Feature Aggregation Network Based on Channel-Spatial Attention for Remote Sensing Scene Classification

Convolutional Neural Networks (CNNs) have been shown remarkable performance in the task of remote sensing image scene classification. Recent works demonstrate that aggregating multi-scale convolutional features can significantly improve the classification accuracy. However, existing methods either use some unsupervised feature encoding methods or based on feature aggregation methods to aggregate multi -scale convolutional features, ignoring the information redundancy and semantic ambiguity between of them. To address the above-mentioned limitations, an end-to-end multi-scale feature aggregation network (MSF A) based on channel-spatial attention module is proposed to learn discriminative scene representation for remote sensing scene classification. The experimental results on the aerial image data set (AID) demonstrate that the proposed method achieves competitive classification performance compared with other state-of-the-art methods.