CDCCNet: Cross-Domain Consistency-Constrained Domain Generalization Network for Remote Sensing Optical Image Change Detection

Abstract

Remote sensing change detection (RSCD) aims to identify and localize changes in the same geographical region using bi-temporal or multitemporal images. However, significant feature distribution shifts commonly exist not only between training and real-world data but also between paired images acquired at different times within the same dataset, severely limiting model generalization. Domain-generalized RSCD seeks to learn domain-invariant representations from source domains (seen), enabling direct deployment to target domains (unseen) without requiring target-domain data during training. Existing methods typically attribute performance degradation to style discrepancies and attempt to suppress style variations through feature regularization. However, because style and content information are highly coupled, such strategies often discard critical content representations. In addition, style discrepancies across datasets can further introduce change-domain shifts, which are largely overlooked by existing methods. To address these issues, this article proposes a domain generalization network for RSCD that enables models trained solely on a source domain to generalize effectively to target domains. Specifically, a feature constraint (FC) mechanism is introduced at the encoder stage to mitigate style interference through covariance alignment while preserving essential content information. At the decoder stage, a cross-domain learning (CDL) module is designed to construct a more discriminative embedding space and separate features prone to misclassification. Extensive experiments demonstrate that the proposed method achieves strong robustness and superior detection accuracy across both source and target domains, significantly outperforming existing methods.