  Advanced change detection techniques primarily target image pairs of equal
and high quality. However, variations in imaging conditions and platforms
frequently lead to image pairs with distinct qualities: one image being
high-quality, while the other being low-quality. These disparities in image
quality present significant challenges for understanding image pairs
semantically and extracting change features, ultimately resulting in a notable
decline in performance. To tackle this challenge, we introduce an innovative
training strategy grounded in knowledge distillation. The core idea revolves
around leveraging task knowledge acquired from high-quality image pairs to
guide the model's learning process when dealing with image pairs that exhibit
differences in quality. Additionally, we develop a hierarchical correlation
distillation approach (involving self-correlation, cross-correlation, and
global correlation). This approach compels the student model to replicate the
correlations inherent in the teacher model, rather than focusing solely on
individual features. This ensures effective knowledge transfer while
maintaining the student model's training flexibility.
