Adapting Segment Anything Model for Change Detection in VHR Remote Sensing Images

Vision foundation models (VFMs), such as the segment anything model (SAM), allow zero-shot or interactive segmentation of visual contents; thus, they are quickly applied in a variety of visual scenes. However, their direct use in many remote sensing (RS) applications is often unsatisfactory due to the special imaging properties of RS images (RSIs). In this work, we aim to utilize the strong visual recognition capabilities of VFMs to improve change detection (CD) in very high-resolution (VHR) RSIs. We employ the visual encoder of FastSAM, a variant of the SAM, to extract visual representations in RS scenes. To adapt FastSAM to focus on some specific ground objects in RS scenes, we propose a convolutional adaptor to aggregate the task-oriented change information. Moreover, to utilize the semantic representations that are inherent to SAM features, we introduce a task-agnostic semantic learning branch to model the semantic latent in bitemporal RSIs. The resulting method, SAM-based CD (SAM-CD), obtains superior accuracy compared with the state-of-the-art (SOTA) fully supervised CD methods and exhibits a sample-efficient learning ability that is comparable to semisupervised CD methods. To the best of our knowledge, this is the first work that adapts VFMs to CD in VHR RSIs.