A Triplet Multi-Task Learning Network for Semantic Change Detection

Semantic change detection (SCD) aims to provide the change locations and extend the detailed semantic change categories before and after the observation intervals, being a pivotal task in remote sensing community. Recent studies indicate that multi-task learning paradigm is an efficient solution for modeling the SCD. However, the temporal dependency information and semantic feature separability are not efficiently explored. To overcome the above limitations, this paper proposes a new Triplet Multi-task learning Network (TMNet) for SCD, in which a differential image branch is introduced to extract the temporal information reflecting the difference in original image pairs and two temporal branch to extract the semantic representation of each image. Then, a class-wise contrastive loss is employed to deal with the change types imbalance and improve the category discrimination. Finally, experiments are carried on a public SCD dataset to demonstrated the effectiveness of the proposed method.