SCECNet: self-correction feature enhancement fusion network for remote sensing scene classification

Remote sensing images exhibit significant variations in target scale and complex backgrounds, as well as distinct differences within classes and high similarities between classes. These characteristics present particular challenges for remote sensing scene classification tasks. To address these issues, this paper proposes an efficient system architecture, the self-correction feature enhancement fusion network (SCECNet), designed to improve scene image processing capabilities. First, a feature pyramid network (FPN) based on ResNet50 is employed as the backbone for feature extraction, which helps alleviate feature loss for small targets. Second, a novel lightweight channel attention mechanism is designed to reduce the differences between features from different layers while suppressing irrelevant information. Next, a self-correction feature fusion module (SCFF) is constructed to further emphasise the main targets in complex environments through adaptive weighting. Finally, the classifier performs the final scene classification. Furthermore, a regional dataset, AHNR-18, is constructed to validate the generalisation capability of SCECNet and supplement existing datasets. Experiments on two benchmark datasets show that our method outperforms several existing methods.