Change detection has always been a concerned task in the interpretation of remote sensing images. It is essentially a unique binary classification task with two inputs, and there is a change relationship between these two inputs. At present, the mining of change relationship features is usually implicit in the network architectures that contain single-branch or two-branch encoders. However, due to the lack of artificial prior design for change relationship features, these networks cannot learn enough change semantic information and lose more accurate change detection performance. So we propose a network architecture NAME for the explicit mining of change relation features. In our opinion, the change features of change detection should be divided into pre-changed image features, post-changed image features and change relation features. In order to fully mine these three kinds of change features, we propose the triple branch network combining the transformer and convolutional neural network (CNN) to extract and fuse these change features from two perspectives of global information and local information, respectively. In addition, we design the continuous change relation (CCR) branch to further obtain the continuous and detail change relation features to improve the change discrimination capability of the model. The experimental results show that our network performs better, in terms of F1, IoU, and OA, than those of the existing advanced networks for change detection on four public very high-resolution (VHR) remote sensing datasets. Our source code is available at https://github.com/DalongZ/NAME.

翻译：变化检测一直是遥感影像解译中备受关注的任务。本质上，这是一项具有两个输入的特殊二分类任务，而这两个输入之间存在变化关系。目前，变化关系特征的挖掘通常隐含在包含单分支或双分支编码器的网络架构中。然而，由于缺乏对变化关系特征的人为先验设计，这些网络无法学习到足够的变化语义信息，导致变化检测性能不够精确。因此，我们提出了一种名为NAME的网络架构，用于显式挖掘变化关系特征。我们认为，变化检测的变化特征应分为变化前影像特征、变化后影像特征和变化关系特征。为了充分挖掘这三类变化特征，我们提出了结合Transformer与卷积神经网络(CNN)的三分支网络，分别从全局信息和局部信息两个角度提取并融合这些变化特征。此外，我们设计了连续变化关系(CCR)分支，进一步获取连续且细致的变化关系特征，以提升模型的变化判别能力。实验结果表明，在四个公开的甚高分辨率(VHR)遥感数据集上，我们的网络在F1分数、IoU和OA指标上均优于现有的先进变化检测网络。我们的源代码可在https://github.com/DalongZ/NAME获取。