通过语义不变自蒸馏学习语义鲁棒的变化检测
Learning Semantic-Robust Change Detection via Semantic-Invariant Self-Distillation
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中文总结 AI 辅助
研究针对遥感图像变化检测中模型特征易受非语义变化干扰的问题，提出SCDistill框架，通过语义不变自蒸馏策略和扩散扰动模拟管道，增强语义一致性并扩展数据，提升变化检测性能，在多基准测试中达最优，泛化能力强。
中文摘要 AI 辅助
变化检测旨在识别遥感图像之间的语义变化。然而，模型特征容易受到光照、阴影和大气变化等非语义变化的干扰，导致误报且在实际场景中泛化能力有限。本文提出SCDistill框架，通过语义不变自蒸馏学习语义鲁棒的变化检测。首先引入语义不变自蒸馏策略增强语义一致性，从受干扰但语义一致的数据中学习语义鲁棒性，使变化检测器能提取抗干扰特征。其次设计基于扩散的扰动模拟管道扩展具有非语义变化的配对数据，让模型明确区分语义变化和外观波动，减少非语义干扰导致的误报。实验表明SCDistill在多个语义变化检测基准上达到最优性能，对二元变化检测和变化字幕任务有很强的泛化能力。
英文摘要
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.
发表机构
- Beijing Institute of Technology（北京理工大学）
机构由 AI 辅助整理，请以论文原文为准。
