Enhancing Inter-Class Discrimination for Domain Adaptation of Change Detection

Recent advancements in fully-supervised change detection (CD) have been notable, yet applying CD effectively in unlabeled scenarios remains a challenge. Our study focuses on domain adaptation of change detection (DACD), transferring knowledge from a labeled dataset (source domain) to an unlabeled dataset (target domain). However, we’ve found that this transfer often leads to reduced discrimination between change and non-change classes because of cross-domain discrepancy, adversely affecting CD performance in the target domain. Aiming at it, we propose a source discriminative learning (SDL) method to learn more discriminative interclass feature representations in the source domain, which can improve the transferability of change knowledge across different CD domains. To evaluate its effectiveness, we established two cross-domain CD scenarios using well-known CD datasets and applied our SDL method to them. The experimental results affirm that our method effectively boosts DACD performance.