Enhanced Multitask Semantic Change Detection via Semi-Supervised Learning in LULC Segmentation Subtask

Change detection (CD) plays a crucial role in remote sensing analysis. Semantic change detection (SCD) further expands CD by incorporating land use and land cover (LULC) segmentation before and after the changes, identifying specific change categories alongside the change area detection. While recent studies combining change area detection and bitemporal LULC segmentation within a multitask framework demonstrate promising performance, they often only utilize pixels in changed areas with change classification labels in LULC segmentation training, overlooking substantial unlabeled LULC data in unchanged areas, which restricts the model's effectiveness. Accordingly, we propose an enhanced multitask SCD method with semi-supervised learning in LULC segmentation, effectively leveraging the extensive unlabeled LULC data and improving the overall performance of the multitask framework. Besides, we introduce a novel loss tailored for this semi-supervised method based on the unique relationship between bitemporal pixel labels in change areas and change classification. Optimization of the semi-supervised loss weighting further refines the training. Experiments on the public dataset validate the effectiveness of these improvements, especially in enhancing change classification performance. Applying our method to the naive model yields improvements in SeK and Fscd, with the increases of up to 2%. The code will be available after the acceptance at https://github.com/ijnokml/scd-enhanced.