Remote-sensing (RS) Change Detection (CD) aims to detect "changes of interest" from co-registered bi-temporal images. The performance of existing deep supervised CD methods is attributed to the large amounts of annotated data used to train the networks. However, annotating large amounts of remote sensing images is labor-intensive and expensive, particularly with bi-temporal images, as it requires pixel-wise comparisons by a human expert. On the other hand, we often have access to unlimited unlabeled multi-temporal RS imagery thanks to ever-increasing earth observation programs. In this paper, we propose a simple yet effective way to leverage the information from unlabeled bi-temporal images to improve the performance of CD approaches. More specifically, we propose a semi-supervised CD model in which we formulate an unsupervised CD loss in addition to the supervised Cross-Entropy (CE) loss by constraining the output change probability map of a given unlabeled bi-temporal image pair to be consistent under the small random perturbations applied on the deep feature difference map that is obtained by subtracting their latent feature representations. Experiments conducted on two publicly available CD datasets show that the proposed semi-supervised CD method can reach closer to the performance of supervised CD even with access to as little as 10% of the annotated training data. Code available at https://github.com/wgcban/SemiCD

翻译：远程遥感(RS) 变化检测(CD) 旨在检测共同注册的双时相图像的“兴趣变化” 。 现有受到严密监督的CD方法的绩效归功于大量用于培训网络的附加说明的数据。 然而,大量遥感图像的注意是劳动密集型和昂贵的,特别是双时相图像,因为它需要由一位人类专家进行像素-角度的比较。 另一方面,我们常常能够获取无限制的、无标记的多时相图像,因为地球观测程序不断增加。在本文中,我们提出了一个简单而有效的方法来利用来自未标记的双时相图像的信息来改进网络的绩效。更具体地说,我们建议采用半超光化的CD模型,在监督的交叉 Entropy (CE) 损失之外,我们制定一种不受监督的CD损失。通过限制给一个未标记的双时相图像配的输出变化概率映射图,以便与在深度地貌差异图上应用的小随机图像保持一致。 CD- 即使是在深度的深度特征图上,通过更接近的CD- CD- surview CD- develop revalateal 将CD 数据显示为更接近的CD- CD- CD- CD- 的CD- proview dal 。 进行实验性能的CD- 演示, 以更接近的CD- dealddal 10 演示的CD