A Semantic Change Detection Network Based on Boundary Detection and Task Interaction for High-Resolution Remote Sensing Images

원문 초록

Semantic change detection (CD) not only helps pinpoint the locations where changes occur, but also identifies the specific types of changes in land cover and land use. Currently, the mainstream approach for semantic CD (SCD) decomposes the task into semantic segmentation (SS) and CD tasks. Although these methods have achieved good results, they do not consider the incentive effect of task correlation on the entire model. Given this issue, this article further elucidates the SCD task through the lens of multitask learning theory and proposes a semantic change detection network based on boundary detection and task interaction (BT-SCD). In BT-SCD, the boundary detection (BD) task is introduced to enhance the correlation between the SS task and the CD task in SCD, thereby promoting positive reinforcement between SS and CD tasks. Furthermore, to enhance the communication of information between the SS and CD tasks, the pixel-level interaction strategy and the logit-level interaction strategy are proposed. Finally, to fully capture the temporal change information of the bitemporal features and eliminate their temporal dependency, a bidirectional change feature extraction module is proposed. Extensive experimental results on three commonly used datasets and a nonagriculturalization dataset (NAFZ) show that our BT-SCD achieves state-of-the-art performance.