论文标题
通过基于图的知识补充网络从合成孔径雷达图像更改检测
Change Detection from Synthetic Aperture Radar Images via Graph-Based Knowledge Supplement Network
论文作者
论文摘要
在遥感图像分析领域，合成孔径雷达（SAR）图像变化检测是一项至关重要但具有挑战性的任务。大多数以前的作品都采用了一种自我监督的方法，该方法使用伪标记的样本指导后续培训和测试。但是，深网通常需要许多高质量的样本才能优化参数。伪标签中的噪声不可避免地会影响最终的变化检测性能。为了解决问题，我们提出了一个基于图的知识补充网络（GKSNET）。更具体地说，我们从现有标记的数据集中提取歧视性信息作为附加知识，以抑制嘈杂样本的不利影响。之后，我们设计一个图形传输模块，以专注于从标记的数据集到目标数据集的上下文信息，桥梁在数据集之间具有相关性。为了验证提出的方法，我们在四个SAR数据集上进行了广泛的实验，与几个最新的基线相比，这证明了所提出的GKSNET的优越性。我们的代码可在https://github.com/summitgao/sar_cd_gksnet上找到。
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.
