HiCD: Change Detection in Quality-Varied Images via Hierarchical Correlation Distillation
This addresses a practical problem in remote sensing and surveillance where image quality varies due to different conditions, though it is an incremental improvement over existing change detection methods.
The paper tackles change detection in image pairs with varying quality by introducing a hierarchical correlation distillation approach that transfers knowledge from high-quality pairs to guide learning on mixed-quality pairs, achieving state-of-the-art performance with improvements of 2.3% in F1 score and 2.1% in IoU on benchmark datasets.
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.