MSF-Net: A Multiscale Supervised Fusion Network for Building Change Detection in High-Resolution Remote Sensing Images

Building change detection is a primary task in the application of remote sensing images, especially in city land resource management and urbanization process assesment. Due to the rich textural features of remote sensing images and the multiscale characteristics of buildings, it is still a huge challenge to effectively filter out irrelevant change information (e.g., roads) and fuse multiscale building features. To date, deep learning-based methods have demonstrated powerful capabilities in this field. To fill these gaps, this study proposes a multiscale supervised fusion network (MSF-Net), which is an attention mechanism-based approach for building change detection using bi-temporal high-resolution satellite imagery. Especially, we built a dual-context fusion module to obtain abundant global context information of buildings and suppressing irrelevant features. We also used channel attention mechanism, selective kernel convolution and multiscale supervision module to fuse multiscale feature of buildings. The ablation experiments verified the availability of these modules. The MSF-Net model has been tested on the LEVIR-CD building change detection dataset. Compared with other state-of-the-art change detection methods, the study showed that our method obtained 0.8866 and 0.8130 in F1-score and Intersection over Union (IOU), respectively. The results indicate that the MSF-Net method has stronger multiscale building feature extraction capability and suppression ability of irrelevant features, which could produce clearer building boundaries and more accurate building change maps.

Automated summary

This study proposes a deep learning-based multiscale supervised fusion network (MSF-Net) for building change detection. By utilizing a dual-context fusion module, channel attention mechanism, selective kernel convolution, and multiscale supervision module, the method can effectively extract building features, suppress irrelevant features, and generate more accurate building change maps.