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.

翻译：先进的变化检测技术主要针对等质量且高质量的图像对。然而，成像条件与平台的差异常导致图像对具有不同质量：一幅图像为高质量，另一幅为低质量。这种图像质量差异给语义理解图像对及提取变化特征带来了显著挑战，最终导致性能明显下降。为解决这一挑战，我们提出了一种基于知识蒸馏的创新训练策略。其核心思想在于利用从高质量图像对中获取的任务知识，指导模型处理存在质量差异的图像对时的学习过程。此外，我们开发了一种层级相关性蒸馏方法（包含自相关性、互相关性与全局相关性）。该方法促使学生模型复现教师模型中的相关性（而非仅关注单个特征），从而在保持学生模型训练灵活性的同时确保有效的知识迁移。