TBSCD-Net: A Siamese Multitask Network Integrating Transformers and Boundary Regularization for Semantic Change Detection From VHR Satellite Images

Semantic change detection (SCD) from very high-resolution images involves two key challenges: (1) the global features of bitemporal images tend to be extracted insufficiently, leading to imprecise land cover semantic classification results, and (2) the detected changed objects exhibit ambiguous boundaries, resulting in low geometric accuracy. To address these two issues, we propose an SCD method called TBSCD-Net based on a multi-task learning framework to simultaneously identify different types of semantic changes and regularize change boundaries. Firstly, we construct a hybrid encoder combining transformer and convolutional neural network (TCEncoder) to enhance the extraction of global context information. A bitemporal semantic linkage module (Bi-SLM) is embedded into the TCEncoder to enhance the semantic correlations between bitemporal images. Secondly, we introduce a boundary-region joint extractor based on Laplacian operators (LOBRE) to regularize the changed objects. We evaluated the effectiveness of the proposed method using the SECOND dataset and a Fuzhou GF-2 SCD dataset (FZ-SCD) and compared it with seven existing methods. The proposed method performed better than the other evaluated methods as it achieved 24.42% Sek and 20.18% GTC on the SECOND dataset and 23.10% Sek and 23.15% GTC on the FZ-SCD dataset. The results of ablation studies on the FZ-SCD dataset also verified the effectiveness of the developed modules for SCD.