  Change detection (CD) by comparing two bi-temporal images is a crucial task
in remote sensing. With the advantages of requiring no cumbersome labeled
change information, unsupervised CD has attracted extensive attention in the
community. However, existing unsupervised CD approaches rarely consider the
seasonal and style differences incurred by the illumination and atmospheric
conditions in multi-temporal images. To this end, we propose a change detection
with domain shift setting for remote sensing images. Furthermore, we present a
novel unsupervised CD method using a light-weight transformer, called
UCDFormer. Specifically, a transformer-driven image translation composed of a
light-weight transformer and a domain-specific affinity weight is first
proposed to mitigate domain shift between two images with real-time efficiency.
After image translation, we can generate the difference map between the
translated before-event image and the original after-event image. Then, a novel
reliable pixel extraction module is proposed to select significantly
changed/unchanged pixel positions by fusing the pseudo change maps of fuzzy
c-means clustering and adaptive threshold. Finally, a binary change map is
obtained based on these selected pixel pairs and a binary classifier.
Experimental results on different unsupervised CD tasks with seasonal and style
changes demonstrate the effectiveness of the proposed UCDFormer. For example,
compared with several other related methods, UCDFormer improves performance on
the Kappa coefficient by more than 12\%. In addition, UCDFormer achieves
excellent performance for earthquake-induced landslide detection when
considering large-scale applications. The code is available at
\url{https://github.com/zhu-xlab/UCDFormer}
