MAFEN: Multiattention Feature Extraction Network for Remote Sensing Scene Classification

The scene classification of high-resolution remote sensing images (HRSIs) provides crucial information for the high-level semantic understanding of the complicated earth's surface. Some existing methods concatenate local features with global features to complete the classification task. However, simple feature concatenation cannot fully exploit the advantages of the above two features. This study proposes a novel multiattention feature extraction network (MAFEN) for the scene classification of HRSIs. First, a dual-branch network is proposed to extract local and global features from the scene images. Subsequently, an interattention fusion module is utilized to merge the local and global features. In addition, the multilevel feature fusion module helps avoid the loss of shallow and middle-layer features. Finally, an aggregated loss is employed to enhance the feature fusion of the dual-stream feature extractor by considering the contributions of different features. Experimental evaluations are conducted on three widely used public datasets for scene classification. Results confirm the potential of MAFEN for remote sensing scene classification tasks when compared with currently advanced methods in the field.