Change Detection from Synthetic Aperture Radar Images via Graph-Based Knowledge Supplement Network
This work addresses a domain-specific challenge in remote sensing image analysis for change detection, offering an incremental improvement by mitigating noise in pseudo-labels.
The paper tackles the problem of noisy pseudo-labels in self-supervised change detection from synthetic aperture radar images by proposing a Graph-based Knowledge Supplement Network (GKSNet), which extracts discriminative information from labeled datasets to suppress noise and demonstrates superiority over state-of-the-art baselines on four SAR datasets.
Synthetic aperture radar (SAR) image change detection is a vital yet challenging task in the field of remote sensing image analysis. Most previous works adopt a self-supervised method which uses pseudo-labeled samples to guide subsequent training and testing. However, deep networks commonly require many high-quality samples for parameter optimization. The noise in pseudo-labels inevitably affects the final change detection performance. To solve the problem, we propose a Graph-based Knowledge Supplement Network (GKSNet). To be more specific, we extract discriminative information from the existing labeled dataset as additional knowledge, to suppress the adverse effects of noisy samples to some extent. Afterwards, we design a graph transfer module to distill contextual information attentively from the labeled dataset to the target dataset, which bridges feature correlation between datasets. To validate the proposed method, we conducted extensive experiments on four SAR datasets, which demonstrated the superiority of the proposed GKSNet as compared to several state-of-the-art baselines. Our codes are available at https://github.com/summitgao/SAR_CD_GKSNet.