Weakly Supervised Semantic Change Detection via Label Refinement Framework

Semantic change detection is a meaningful but challenging task in the remote sensing community. The currently dominant approaches are mainly based on deep learning. However, the lack of high-resolution annotations is the main bottleneck for semantic change detection at scale when using these state-of-the-art deep learning models. In this paper, the label refinement framework is proposed for weakly-supervised semantic change detection, which allows the deep network to learn from low-resolution labels and produce high-resolution semantic change maps, thus alleviating the data-hungry problem. This framework contains four parts: coarse label training’ pseudo-label refinement, multitask change detection and post-process. The experimental results on 2021 IEEE GRSS Data Fusion Contest Track MSD dataset confirmed the effectiveness of the proposed method. Additionally, our method wins 4th place in the 2021 IEEE GRSS Data Fusion Contest Track MSD (DFC21-MSD).