MINet: Multilevel Inheritance Network-Based Aerial Scene Classification

Abstract

Scene classification of aerial images is the basis of automatic recognition of complex scenes, and it is also a challenging computer vision task. In recent years, with the rapid development of deep learning, the semantic feature extraction method based on a convolutional neural network (CNN) has made great progress. Moreover, a recent study indicates that combining the semantic information of deep-layer features with the detailed texture information of shallow-layer features in CNN can further improve the performance of classification. In this letter, an end-to-end multilevel feature-based network named multilevel inheritance network (MINet) is proposed for aerial scene classification. First, the feature extraction module based on the feature pyramid network (FPN) is used to get multilevel feature maps. In the process of merging shallow features, high-level semantics of deep-layer are inherited. Then, an attention mechanism is added after the multilevel features to reduce the interference of redundant information and noise. Finally, we use a feature fusion module to automatically learn the weight of each feature layer and make a comprehensive decision. The effectiveness of the proposed method is verified in AID, WHU-RS19 and NWPU-RESISC45 datasets. Results show that the proposed method achieves competitive classification accuracy.