UnlearningCD: Distill to Forget in Change Detection via Perturbed Knowledge

Change detection (CD) in remote sensing plays a vital role in Earth observation. As deep learning models and associated data grow exponentially, privacy concerns, outdated or erroneous annotations have intensified. Machine unlearning offers a solution by enabling models to selectively discard specific data. However, there is limited exploration of unlearning in remote sensing. This paper introduces an Unlearning algorithm based on Perturbed Knowledge distillation (UPK) specifically designed for CD tasks. Two variants are proposed: closed-box and open-box, addressing scenarios with varying levels of model access. Our approach injects perturbations into teacher models to guide students, achieving targeted unlearning while preserving detection capabilities for other change types. Extensive experiments conducted on semantic CD (SCD) and building damage assessment (BDA) datasets demonstrate that UPK requires only 10% of the training iterations needed for original models and outperforms existing strategies across different CD tasks and model architectures, without incurring additional inference time. This study highlights UPK's potential as a scalable solution for effective model lifecycle management in the rapidly evolving remote sensing fields.