Change detection, which aims to detect spatial changes from a pair of
multi-temporal images due to natural or man-made causes, has been widely
applied in remote sensing, disaster management, urban management, etc. Most
existing change detection approaches, however, are fully supervised and require
labor-intensive pixel-level labels. To address this, we develop a novel weakly
supervised change detection technique via Knowledge Distillation and Multiscale
Sigmoid Inference (KD-MSI) that leverages image-level labels. In our approach,
the Class Activation Maps (CAM) are utilized not only to derive a change
probability map but also to serve as a foundation for the knowledge
distillation process. This is done through a joint training strategy of the
teacher and student networks, enabling the student network to highlight
potential change areas more accurately than teacher network based on
image-level labels. Moreover, we designed a Multiscale Sigmoid Inference (MSI)
module as a post processing step to further refine the change probability map
from the trained student network. Empirical results on three public datasets,
i.e., WHU-CD, DSIFN-CD, and LEVIR-CD, demonstrate that our proposed technique,
with its integrated training strategy, significantly outperforms the
state-of-the-art.

这篇论文介绍了一种基于知识蒸馏和多尺度 Sigmoid 推理的弱监督变化检测技术，通过利用图像级别标签，能够更准确地检测潜在的变化区域，进一步提高了变化概率图的性能。实证结果表明，该方法在三个公共数据集上明显优于现有技术。