Integrating Segment Anything Model With Instance-Level Change Generation for Single-Temporal Unsupervised Change Detection

Implementing change detection (CD) with bitemporal remote sensing images (RSIs) is essential for gaining insights into the dynamic evolution of the Earth’s surface. Recent advancements in deep learning methodologies have demonstrated considerable success in CD applications. However, the efficacy of supervised CD networks is significantly constrained by their dependence on high-quality change labels and precisely registered bitemporal RSIs, which impose significant limitations in real-world scenarios. To address this issue, we propose a novel single-temporal unsupervised CD method, STU-SAMI, which integrates the segment anything model (SAM) with instance-level change generation. This method aims to facilitate the training of high-performance CD networks using unpaired and unlabeled single-temporal RSIs. The proposed approach comprises three main steps. First, SAM is introduced to extract morphologically complete object instances from single-temporal RSIs. Second, an online instance-level change generation (OICG) method is proposed, which transforms single-temporal RSIs into bitemporal pseudo change samples based on the object instances obtained by SAM. Third, a streamlined and efficient deep Siamese CD network is constructed to support the training and inference processes. Extensive experiments conducted on three benchmark datasets demonstrate that the proposed method outperforms the existing state-of-the-art unsupervised CD methods. The code is available at: https://github.com/IceStreams/STU-SAMI