The purpose of remote sensing image change detection (RSCD) is to detect differences between bi-temporal images taken at the same place. Deep learning has been extensively used to RSCD tasks, yielding significant results in terms of result recognition. However, due to the shooting angle of the satellite, the impacts of thin clouds, and certain lighting conditions, the problem of fuzzy edges in the change region in some remote sensing photographs cannot be properly handled using current RSCD algorithms. To solve this issue, we proposed a Body Decouple Multi-Scale by fearure Aggregation change detection (BD-MSA), a novel model that collects both global and local feature map information in the channel and space dimensions of the feature map during the training and prediction phases. This approach allows us to successfully extract the change region's boundary information while also divorcing the change region's main body from its boundary. Numerous studies have shown that the assessment metrics and evaluation effects of the model described in this paper on the publicly available datasets DSIFN-CD and S2Looking are the best when compared to other models.

翻译：遥感影像变化检测（RSCD）旨在检测同一地点的双时相影像之间的差异。深度学习已广泛用于RSCD任务，在结果识别方面取得了显著成效。然而，由于卫星拍摄角度、薄云影响及特定光照条件，现有RSCD算法难以妥善处理部分遥感影像中变化区域的边缘模糊问题。为解决这一问题，我们提出了一种基于特征聚合的体解耦多尺度变化检测方法（BD-MSA），该模型在训练和预测阶段同时采集特征图在通道与空间维度上的全局与局部特征图信息。该方法既能成功提取变化区域的边界信息，又能将变化区域的主体与其边界解耦。大量研究表明，本文所述模型在公开数据集DSIFN-CD和S2Looking上的评估指标与效果均优于其他对比模型。