  The field of Remote Sensing (RS) widely employs Change Detection (CD) on
very-high-resolution (VHR) images. A majority of extant deep-learning-based
methods hinge on annotated samples to complete the CD process. Recently, the
emergence of Vision Foundation Model (VFM) enables zero-shot predictions in
particular vision tasks. In this work, we propose an unsupervised CD method
named Segment Change Model (SCM), built upon the Segment Anything Model (SAM)
and Contrastive Language-Image Pre-training (CLIP). Our method recalibrates
features extracted at different scales and integrates them in a top-down manner
to enhance discriminative change edges. We further design an innovative
Piecewise Semantic Attention (PSA) scheme, which can offer semantic
representation without training, thereby minimize pseudo change phenomenon.
Through conducting experiments on two public datasets, the proposed SCM
increases the mIoU from 46.09% to 53.67% on the LEVIR-CD dataset, and from
47.56% to 52.14% on the WHU-CD dataset. Our codes are available at
https://github.com/StephenApX/UCD-SCM.
