Multi-class remote sensing change detection based on model fusion

Change detection in remote sensing images has an important impact in various application fields. In recent years, great progress has been made in the change detection methods of multiple types of ground objects, but there are still limited recognition capabilities of the extracted features, resulting in unclear boundaries, and the accuracy rate needs to be improved. To address these issues, we use a high-resolution network (HRNet) to generate high-resolution representations and add new data augmentation methods to improve its accuracy. Secondly, we introduce the model of Transformer structure -- CSWin and HRNet to fuse to improve the performance and effect of the model. In order to enhance the model's ability to perceive ground objects at different scales, a feature fusion network suitable for multi-class semantic segmentation is designed, named A-FPN. This feature fusion network is introduced between the CSWin backbone network and the semantic segmentation network. The experimental results show that the fusion method greatly improves the accuracy to 89.31% on the SECOND dataset, significantly reduces false detections, and recognizes the edges of objects more clearly. And achieved good results in the three evaluation indicators of precision, recall, and F1-score.