  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 characteristics of RS images. In this work, we aim to utilize
the strong visual recognition capabilities of VFMs to improve the change
detection of high-resolution Remote Sensing Images (RSIs). We employ the visual
encoder of FastSAM, an efficient variant of the SAM, to extract visual
representations in RS scenes. To adapt FastSAM to focus on some specific ground
objects in the 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 bi-temporal RSIs. The
resulting method, SAMCD, obtains superior accuracy compared to the SOTA methods
and exhibits a sample-efficient learning ability that is comparable to
semi-supervised CD methods. To the best of our knowledge, this is the first
work that adapts VFMs for the CD of HR RSIs.
