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

• URL: http://arxiv.org/abs/2607.19000v1
• Date: Tue, 21 Jul 2026 11:33:11 GMT
• Title: Learning Semantic-Robust Change Detection via Semantic-Invariant Self-Distillation
• Abstract summary: SCDistill is a framework for learning semantic-robust change detection via semantic-invariant self-distillation.<n>It achieves state-of-the-art performance on multiple semantic change detection benchmarks and exhibits strong generalization to binary change detection and change captioning tasks.
• Score: 16.080049512272026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Change detection aims to identify semantic changes between remote sensing images. However, features from models are easily disturbed by non-semantic variations, such as illumination, shadows, and atmospheric changes, leading to false alarms and limited generalization in real-world scenarios. In this paper, we propose \textbf{SCDistill}, a framework for learning semantic-robust change detection via semantic-invariant self-distillation. First, to strengthen semantic consistency, we introduce a semantic-invariant self-distillation strategy that learns semantic robustness from perturbed yet semantically consistent data, empowering the change detector to extract disturbance-resistant features and achieve more reliable and accurate semantic change identification. Second, to expand paired data with non-semantic variations, we design a diffusion-based perturbation simulation pipeline that synthesizes complex environmental changes, enabling the model to explicitly learn to distinguish semantic changes from appearance-level fluctuations and reduce false alarms caused by non-semantic disturbances. These components promote robustness from data and representation perspectives, leading to synergistic performance gains. Extensive experiments demonstrate that SCDistill achieves state-of-the-art performance on multiple semantic change detection benchmarks and exhibits strong generalization to binary change detection and change captioning tasks. Code is accessible at https://github.com/elecreak/SCDistill.

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