论文标题
自我对：从单源中的综合变化，用于遥感图像中的对象变化检测
Self-Pair: Synthesizing Changes from Single Source for Object Change Detection in Remote Sensing Imagery
论文作者
论文摘要
对于遥感中的变更检测，由于双期监督的要求，很难为深度学习模型构建培训数据集。为了克服这个问题，已经提出了将更改标签视为两个语义面具的差异的单个时空监督。这种新颖的方法使用两个带有相应语义标签（例如构建）的空间无关图像来训练变更探测器。但是，在未配对数据集上进行培训可能会使变更探测器混淆，如果像素标记不变但在视觉上有显着差异。为了保持不变区域中的视觉相似性，我们强调了变化源于源图像，并表明将源图像作为后图进行操作对于变更检测的性能至关重要。广泛的实验表明，在事件前和事后图像之间保持视觉信息的重要性，而我们的方法优于基于单个颞监管的现有方法。代码可从https://github.com/seominseok0429/self-pair-for-change-detection获得。
For change detection in remote sensing, constructing a training dataset for deep learning models is difficult due to the requirements of bi-temporal supervision. To overcome this issue, single-temporal supervision which treats change labels as the difference of two semantic masks has been proposed. This novel method trains a change detector using two spatially unrelated images with corresponding semantic labels such as building. However, training on unpaired datasets could confuse the change detector in the case of pixels that are labeled unchanged but are visually significantly different. In order to maintain the visual similarity in unchanged area, in this paper, we emphasize that the change originates from the source image and show that manipulating the source image as an after-image is crucial to the performance of change detection. Extensive experiments demonstrate the importance of maintaining visual information between pre- and post-event images, and our method outperforms existing methods based on single-temporal supervision. code is available at https://github.com/seominseok0429/Self-Pair-for-Change-Detection.
