Abstract
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 this https URL
Abstract (translated)
遥感变化检测(RS-CD)旨在从多时间遥感图像(MT-RSIs)中检测相关变化,帮助各种遥感应用,例如植被覆盖、土地使用、人类发展分析、灾难响应。现有RS-CD方法的性能归咎于大规模标注数据的训练。此外,这些模型通常不太可转移,因为训练模型通常在训练和测试数据集之间存在领域差距时表现很差。本文提出了基于深度度量学习的无监督CD方法,可以处理这两个问题。给定一个MT-RSI,该方法通过迭代优化无监督CD损失而不需要在大规模数据集上训练它。我们的无监督CD方法由两个相互连接的深度网络组成,分别是深度变化概率生成(D-CPG)和深度特征提取(D-FE)。D-CPG旨在预测给定MT-RSI的变化和不变概率地图,而D-FE用于提取MT-RSI的深度特征,这些特征将用于提议的无监督CD损失。我们使用转移学习能力初始化D-FE参数。我们迭代优化D-CPG和D-FE对给定MT-RSI的参数,通过最小化提议的无监督的“相似度差异损失”。该损失是基于度量学习原则而提出的,我们在双时间图像域和其深度特征域中的最大变化像素之间的相对距离最小化,同时最小化不变像素之间的相对距离。在三个CD数据集上进行的实验表明,我们的无监督CD方法在监督和无监督CD方法之上实现了显著的改进。代码可在这个httpsURL上获取。
URL
https://arxiv.org/abs/2303.09536
