论文标题
基于图像重建损失的无监督变更检测
Unsupervised Change Detection Based on Image Reconstruction Loss
论文作者
论文摘要
为了训练变更探测器，使用在同一区域的不同时间拍摄的双向图像。但是，收集标记的双暂时图像是昂贵且耗时的。为了解决这个问题，已经提出了各种无监督的更改检测方法，但它们仍然需要未标记的双期图像。在本文中，我们建议仅使用未标记的单个时间单图像基于图像重建损失的无监督更改检测。训练图像重建模型通过接收源图像和光度法转换的源图像作为一对来重建原始源图像。在推断期间，该模型接收双暂时图像作为输入，并尝试重建其中一个输入。双向图像之间的变化区域显示出高重建损失。我们的变更检测器在各种更改检测基准数据集中显示出显着的性能，即使仅使用了单个时间源图像。代码和训练有素的模型将公开可重现。
To train the change detector, bi-temporal images taken at different times in the same area are used. However, collecting labeled bi-temporal images is expensive and time consuming. To solve this problem, various unsupervised change detection methods have been proposed, but they still require unlabeled bi-temporal images. In this paper, we propose unsupervised change detection based on image reconstruction loss using only unlabeled single temporal single image. The image reconstruction model is trained to reconstruct the original source image by receiving the source image and the photometrically transformed source image as a pair. During inference, the model receives bi-temporal images as the input, and tries to reconstruct one of the inputs. The changed region between bi-temporal images shows high reconstruction loss. Our change detector showed significant performance in various change detection benchmark datasets even though only a single temporal single source image was used. The code and trained models will be publicly available for reproducibility.
