Remote Sensing Change Detection (RS-CD) aims to detect relevant changes from Multi-Temporal Remote Sensing Images (MT-RSIs), which aids in various RS applications such as land cover, land use, human development analysis, and disaster response. The performance of existing RS-CD methods is attributed to training on large annotated datasets. Furthermore, most of these models are less transferable in the sense that the trained model often performs very poorly when there is a domain gap between training and test datasets. This paper proposes an unsupervised CD method based on deep metric learning that can deal with both of these issues. Given an MT-RSI, the proposed method generates corresponding change probability map by iteratively optimizing an unsupervised CD loss without training it on a large dataset. Our unsupervised CD method consists of two interconnected deep networks, namely Deep-Change Probability Generator (D-CPG) and Deep-Feature Extractor (D-FE). The D-CPG is designed to predict change and no change probability maps for a given MT-RSI, while D-FE is used to extract deep features of MT-RSI that will be further used in the proposed unsupervised CD loss. We use transfer learning capability to initialize the parameters of D-FE. We iteratively optimize the parameters of D-CPG and D-FE for a given MT-RSI by minimizing the proposed unsupervised ``similarity-dissimilarity loss''. This loss is motivated by the principle of metric learning where we simultaneously maximize the distance between change pair-wise pixels while minimizing the distance between no-change pair-wise pixels in bi-temporal image domain and their deep feature domain. The experiments conducted on three CD datasets show that our unsupervised CD method achieves significant improvements over the state-of-the-art supervised and unsupervised CD methods. Code available at https://github.com/wgcban/Metric-CD

翻译：遥感影像变化检测（RS-CD）旨在从多时相遥感影像（MT-RSIs）中检测相关变化，这有助于多种遥感应用，如土地覆盖、土地利用、人类发展分析和灾害响应。现有RS-CD方法的性能归因于在大规模标注数据集上的训练。此外，当训练与测试数据集之间存在域差异时，大多数模型的迁移能力较差，往往表现不佳。本文提出一种基于深度度量学习的无监督变化检测方法，可同时解决这两个问题。给定MT-RSI，该方法通过迭代优化无监督变化检测损失（无需在大数据集上训练）生成对应的变化概率图。我们的无监督变化检测方法由两个相互连接的深度网络组成：深度变化概率生成器（D-CPG）和深度特征提取器（D-FE）。D-CPG用于预测给定MT-RSI的变化与无变化概率图，而D-FE用于提取MT-RSI的深度特征，这些特征将进一步用于所提出的无监督变化检测损失。我们利用迁移学习能力初始化D-FE的参数。通过最小化所提出的无监督“相似-不相似损失”，迭代优化给定MT-RSI的D-CPG与D-FE参数。该损失受度量学习原理启发，我们在双时相图像域及其深度特征域中，同时最大化变化像素对之间的距离，同时最小化无变化像素对之间的距离。在三个变化检测数据集上的实验表明，我们的无监督变化检测方法相较于最先进的监督与无监督变化检测方法取得了显著改进。代码地址：https://github.com/wgcban/Metric-CD