  In the conventional change detection (CD) pipeline, two manually registered
and labeled remote sensing datasets serve as the input of the model for
training and prediction. However, in realistic scenarios, data from different
periods or sensors could fail to be aligned as a result of various coordinate
systems. Geometric distortion caused by coordinate shifting remains a thorny
issue for CD algorithms. In this paper, we propose a reusable self-supervised
framework for bitemporal geometric distortion in CD tasks. The whole framework
is composed of Pretext Representation Pre-training, Bitemporal Image Alignment,
and Down-stream Decoder Fine-Tuning. With only single-stage pre-training, the
key components of the framework can be reused for assistance in the bitemporal
image alignment, while simultaneously enhancing the performance of the CD
decoder. Experimental results in 2 large-scale realistic scenarios demonstrate
that our proposed method can alleviate the bitemporal geometric distortion in
CD tasks.
