Real-CD: Change Detection Under Real-World Complex Interference via Dynamic Distribution Correction.

Abstract

While change detection (CD) is crucial for tracking dynamic changes on the Earth's surface, it faces substantial challenges in real-world settings caused by seasonal variations and sensor-related interference. Current CD models often suffer performance degradation under such conditions, mainly due to two key challenges. First, most existing CD datasets lack sufficient temporal and environmental diversity, as they are typically collected over constrained time spans. This limits the models' ability to generalize across varying conditions. Second, many CD methods are heavily data-driven and rely on simplified assumptions, leading to models that are not adequately designed to handle the complex, heterogeneous nature of real-world scenarios. Together, these challenges restrict the robustness and practical applicability of current CD approaches. To overcome these challenges, we make the following contributions in this paper: 1) Regarding data diversity, we construct a comprehensive benchmark by introducing five typical perturbations (fog, snow, motion blur, Gaussian noise, and impulse noise) into three classical CD datasets and supplementing them with a real-world seasonal dataset, resulting in 75 interference-rich scenarios. This enables a systematic evaluation under diverse real-world conditions, revealing that such perturbations induce severe distribution shifts across both temporal phases and hierarchical network layers, leading to substantial performance degradation in existing models. 2) Algorithmically, we propose Real-CD, a novel method specifically designed to address distribution shifts in real-world CD. The core of Real-CD is to leverage bi-temporal correlations to perform adaptive distribution alignment across hierarchical layers and temporal phases. Specifically, we propose the Distribution Shifts Alleviation Module (DSAM) to correct distribution shifts. The DSAM captures bi-temporal differences and similarities to formulate temporal-specific adjustment strategies for each LayerNorm (LN) layer. To stabilize the optimization of DSAM, we propose the Distribution Consistency Optimization Strategy (DCOS), which introduces a flip-based auxiliary task that encourages the model to maintain distributional consistency under complex bi-temporal disturbances. Consequently, our method outperforms other state-of-the-art approaches and achieves the best performance on the proposed dataset. Our datasets and code implementation will be available at https://github.com/fangyee-ISALAB/Real-CD.