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The field of Remote Sensing (RS) widely employs Change Detection (CD) onvery-high-resolution (VHR) images. A majority of extant deep-learning-basedmethods hinge on annotated samples to complete the CD process. Recently, theemergence of Vision Foundation Model (VFM) enables zero-shot predictions inparticular vision tasks. In this work, we propose an unsupervised CD methodnamed Segment Change Model (SCM), built upon the Segment Anything Model (SAM)and Contrastive Language-Image Pre-training (CLIP). Our method recalibratesfeatures extracted at different scales and integrates them in a top-down mannerto enhance discriminative change edges. We further design an innovativePiecewise Semantic Attention (PSA) scheme, which can offer semanticrepresentation without training, thereby minimize pseudo change phenomenon.Through conducting experiments on two public datasets, the proposed SCMincreases the mIoU from 46.09% to 53.67% on the LEVIR-CD dataset, and from47.56% to 52.14% on the WHU-CD dataset. Our codes are available athttps://github.com/StephenApX/UCD-SCM.

Segment Change Model (SCM) for Unsupervised Change detection in VHR Remote Sensing Images: a Case Study of Buildings