Disentangle to Align: A New Paradigm for Robust Change Detection via Adversarial Feature Purification

Abstract

Remote sensing change detection (RSCD) remains vulnerable to pseudo-changes caused by seasonal, illumination, and atmospheric discrepancies between bi-temporal images. Existing deep models often ignore this temporal distribution gap or align entangled features directly, which may corrupt change-relevant semantics and cause negative transfer. To address this challenge, this article proposes the disentangled adversarial alignment network (DAANet), establishing a novel “Disentangle to Align” paradigm and introducing a domain adaptation (DA)-inspired temporal distribution alignment framework for time-invariant feature learning (IFL) in change detection. DAANet first uses a content-style gate (CSG) to separate content-preserving features from style-biased residuals, and then applies adversarial alignment only to the residual branch. This targeted alignment encourages the backbone to learn time-invariant representations while preserving semantic cues for real changes. A dual-dimensional dynamic balancing strategy further stabilizes the adversarial optimization. Extensive experiments on WHU-CD, LEVIR-CD, SYSU-CD, and MSRSCD, together with backbone-universality analyses, demonstrate the effectiveness and robustness of DAANet under complex imaging conditions.