  Semantic change detection is an important task in geoscience and earth
observation. By producing a semantic change map for each temporal phase, both
the land use land cover categories and change information can be interpreted.
Recently some multi-task learning based semantic change detection methods have
been proposed to decompose the task into semantic segmentation and binary
change detection subtasks. However, previous works comprise triple branches in
an entangled manner, which may not be optimal and hard to adopt foundation
models. Besides, lacking explicit refinement of bitemporal features during
fusion may cause low accuracy. In this letter, we propose a novel late-stage
bitemporal feature fusion network to address the issue. Specifically, we
propose local global attentional aggregation module to strengthen feature
fusion, and propose local global context enhancement module to highlight
pivotal semantics. Comprehensive experiments are conducted on two public
datasets, including SECOND and Landsat-SCD. Quantitative and qualitative
results show that our proposed model achieves new state-of-the-art performance
on both datasets.
