Remote sensing image change detection aims to identify the differences between images acquired at different times in the same area. It is widely used in land management, environmental monitoring, disaster assessment and other fields. Currently, most change detection methods are based on Siamese network structure or early fusion structure. Siamese structure focuses on extracting object features at different times but lacks attention to change information, which leads to false alarms and missed detections. Early fusion (EF) structure focuses on extracting features after the fusion of images of different phases but ignores the significance of object features at different times for detecting change details, making it difficult to accurately discern the edges of changed objects. To address these issues and obtain more accurate results, we propose a novel network, Triplet UNet(T-UNet), based on a three-branch encoder, which is capable to simultaneously extract the object features and the change features between the pre- and post-time-phase images through triplet encoder. To effectively interact and fuse the features extracted from the three branches of triplet encoder, we propose a multi-branch spatial-spectral cross-attention module (MBSSCA). In the decoder stage, we introduce the channel attention mechanism (CAM) and spatial attention mechanism (SAM) to fully mine and integrate detailed textures information at the shallow layer and semantic localization information at the deep layer.

翻译：遥感影像变化检测旨在识别同一区域不同时间获取影像之间的差异，广泛应用于土地管理、环境监测、灾害评估等领域。当前大多数变化检测方法基于孪生网络结构或早期融合结构。孪生结构侧重于提取不同时间的对象特征，但缺乏对变化信息的关注，导致虚警和漏检；早期融合结构则侧重于多时相影像融合后的特征提取，却忽略了不同时间对象特征对检测变化细节的重要性，难以准确辨识变化对象的边缘。为解决这些问题并获得更精确的结果，我们提出一种基于三分支编码器的新型网络——三元组UNet（T-UNet），该网络通过三元组编码器能够同时提取前后时相影像的对象特征与变化特征。为有效交互并融合三元组编码器三个分支提取的特征，我们提出多分支空间-光谱交叉注意力模块（MBSSCA）。在解码器阶段，我们引入通道注意力机制（CAM）和空间注意力机制（SAM），以充分挖掘和整合浅层细节纹理信息与深层语义定位信息。