Weakly Supervised Change Detection via Knowledge Distillation and Multiscale Sigmoid Inference

• URL: http://arxiv.org/abs/2403.05796v1
• Date: Sat, 9 Mar 2024 05:01:51 GMT
• Title: Weakly Supervised Change Detection via Knowledge Distillation and Multiscale Sigmoid Inference
• Authors: Binghao Lu, Caiwen Ding, Jinbo Bi, Dongjin Song
• Abstract summary: We develop a novel weakly supervised change detection technique via Knowledge Distillation and Multiscale Sigmoid Inference (KD-MSI) Our proposed technique, with its integrated training strategy, significantly outperforms the state-of-the-art.
• Score: 26.095501974608908
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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