Learning Semantic-Robust Change Detection via Semantic-Invariant Self-Distillation

Change detection aims to identify semantic changes betweenremote sensing images. However, features from models are easily dis-turbed by non-semantic variations, such as illumination, shadows, andatmospheric changes, leading to false alarms and limited generaliza-tion in real-world scenarios. In this paper, we propose SCDistill, aframework for learning semantic-robust change detection via semantic-invariant self-distillation. First, to strengthen semantic consistency, weintroduce a semantic-invariant self-distillation strategy that learns se-mantic robustness from perturbed yet semantically consistent data, em-powering the change detector to extract disturbance-resistant featuresand achieve more reliable and accurate semantic change identification.Second, to expand paired data with non-semantic variations, we designa diffusion-based perturbation simulation pipeline that synthesizes com-plex environmental changes, enabling the model to explicitly learn todistinguish semantic changes from appearance-level fluctuations and re-duce false alarms caused by non-semantic disturbances. These compo-nents promote robustness from data and representation perspectives,leading to synergistic performance gains. Extensive experiments demon-strate that SCDistill achieves state-of-the-art performance on multiplesemantic change detection benchmarks and exhibits strong generaliza-tion to binary change detection and change captioning tasks. Code isaccessible at https://github.com/elecreak/SCDistill.