M-Swin: Transformer-Based Multiscale Feature Fusion Change Detection Network Within Cropland for Remote Sensing Images

Remote sensing image change detection is extensively utilized in various applications in the field of remote sensing, particularly in the realm of cropland conservation, where it plays a critical role in protecting the agro-ecosystem and ensuring global food security. However, the progressive improvement in resolution and size of remote sensing imagery has led to a 'scale gap' challenge in the detection of small building changes in cropland areas. To address this challenge, an innovative multi-scale feature fusion change detection network (M-Swin) based on transformer using hierarchical windows is proposed. In order to obtain clearer edges and better separation of the change results, a novel saimese transformer encoder (MSW encoder) is proposed, which can better capture the change information in small building through hierarchical windows and fuse the multi-scale feature obtained from different windows. To effectively reduce missed and misdetected small-area of changing buildings, a novel bi-temporal image feature fusion module (BFFM) is proposed, which can enhance the features based on a priori guidance, thus improving the saliency of change regions. Additionally, a new remote sensing image change detection dataset for cropland, called LuojiaSET-CLCD, has been proposed. Experimentally demonstrates that M-Swin has good potential for highly accurate change detection of small buildings within cropland areas and outperforms several newly existing methods in three datasets (LEVIR, WHU-CD and LuojiaSET-CLCD). Our dataset will be publicly available at https://github.com/RSIIPAC/LuojiaSET-CLCD.