A Siamese Network for Semantic Change Detection Based on Multiscale Context Fusion

Bi-temporal semantic change detection(SCD) is more sophisticated than binary change detection and it provides more detailed changing information with categories. Naturally, it is more challenging than traditional binary change detection. In this paper, a Siamese CNN is proposed for SCD. For the problems of complex backgrounds of remote sensing images, we use multiscale context information and correlation to enhance SCD performance. For the problem of insufficient feature utilization between subtasks, a channel fusion module is proposed to explore the temporal correlation between bi-temporal images, which benefits the extraction of the final changing map. The experiments in this paper are conducted on the SECOND dataset. Our proposed method outperforms compared methods and obtains more completed changing maps than other methods.