TCEIGNet: Time-Correlated and Edge Information-Guided Network for Short-Term Building Change Detection

Existing change detection methods mainly focus on bitemporal remote sensing images with a wide time range, which contain several changed areas. However, short-term change detection, which covers a smaller time range, has lower temporal resolution and more unbalanced positive and negative samples, leading to performance degradation. To solve this issue, we propose a multitask framework named Time-Correlated and Edge Information-Guided Network for Short-Term Building Change Detection (TCEIGNet). A time-correlated block is introduced to link the current image with historical information, enhancing the model’s sensitivity to time information. To address the blurring of prediction boundaries and the lack of local details, we propose to use a difference-of-Gaussian (DoG) pyramid to incorporate boundary prior knowledge at each level. Additionally, we add a feature constraint loss to tackle sample imbalance. Experimental results on SpaceNet7 demonstrate that our method outperforms other network methods at different time intervals.