Change detection is a fundamental problem in remote sensing image processing. Due to the great advantages in learning the knowledge representations and the complex relationship from large-scale datasets, deep learning has made great progress in change detection tasks in remote sensing community. However, most of the existing methods based on deep learning for change detection are implemented by learning differences of image pairs directly without paying considerations in influences of unstructured and temporal changes, or called nature changes, such as light and seasonal changes. In this paper, an end-to-end deep learning network for remote sensing image change detection is proposed, aiming to accurately detect the change of regions from a high-resolution image pair by learning prototype similarity, in which the metric learning is used and it is one of the meta-learning methods to learn change prototypes from support image pairs. The similarity between the query image pairs and the change prototypes can be measured by a learnable CNN metric. The experimental results based on the two public change detection datasets of high-resolution satellite images, CDD and BCDD, show that our proposed method performs better than other state-of-the-art change detection methods with an improvement of 3.5% and 0.4%, respectively.

变化检测是遥感图像处理中的一个基本问题。由于从大规模数据集学习知识表示和复杂关系的巨大优势，深度学习在遥感社区的变化检测任务中取得了很大进展。然而，现有的大多数基于深度学习的变化检测方法都是通过直接学习图像对的差异来实现的，而没有考虑非结构化和时间变化的影响，或者称为自然变化，例如光和季节变化。本文提出了一种端到端的遥感图像变化检测深度学习网络，旨在通过学习原型相似性，从高分辨率图像对中准确检测区域的变化，其中使用了度量学习，它是从支持图像对中学习变化原型的元学习方法之一。查询图像对和变化原型之间的相似性可以通过可学习的 CNN 指标来衡量。基于高分辨率卫星图像的两个公共变化检测数据集 CDD 和 BCDD 的实验结果表明，我们提出的方法比其他最先进的变化检测方法性能更好，分别提高了 3.5% 和 0.4% ， 分别。