Uncertainty-Guided Causal Disentanglement Network for Remote Sensing Change Detection

Existing remote sensing change detection (RSCD) methods typically rely on intertwined feature representation learning, jointly encoding semantic backgrounds, real changes, and environmental perturbations. This entangled representation couples change indicators with invariant background semantics, rendering models highly susceptible to spurious correlations induced by variations in illumination, seasonal phenology, and sensor discrepancies. Furthermore, existing paradigms lack the capacity to model pixel-level prediction reliability. To address these limitations, this paper proposes an Uncertainty-Guided Causal Disentangled Network for RSCD. We first formalize the RSCD process via a Structural Causal Model (SCM), establishing a causal generative graph governing invariant backgrounds, authentic semantic changes, and confounding noise, and theoretically demonstrating that explicit feature disentanglement effectively blocks the backdoor path through which confounders bias change inference. Building upon this formulation, we design a Pearson correlation-guided feature disentanglement that distills shared invariant representations via cross-temporal correlations and explicitly segregates change components from background factors. To enhance the discriminative capacity of the disentangled representations, we further introduce a maximum-entropy clustering-based disentangled prototypical contrastive learning strategy. Unlike conventional prototype learning that forces all features to rigidly converge toward a single prototype, this strategy guides initial prototypes toward core prototypes, thereby better preserving the multimodal topology and diversity of real-world change manifolds. Finally, to bolster feature confidence, a progressive evidence-accumulating uncertainty estimation module is incorporated. By parameterizing a Dirichlet distribution over the disentangled representations, this module not only quantifies predictive uncertainty but also optimizes epistemic reliability via an evidence-based loss. Extensive experiments demonstrate that the proposed framework achieves substantial improvements across both qualitative and quantitative benchmarks.