  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, S2Looking and WHU-CD are the best
when compared to other models.
