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The purpose of remote sensing image change detection (RSCD) is to detectdifferences between bi-temporal images taken at the same place. Deep learninghas been extensively used to RSCD tasks, yielding significant results in termsof result recognition. However, due to the shooting angle of the satellite, theimpacts of thin clouds, and certain lighting conditions, the problem of fuzzyedges in the change region in some remote sensing photographs cannot beproperly handled using current RSCD algorithms. To solve this issue, weproposed a Body Decouple Multi-Scale by fearure Aggregation change detection(BD-MSA), a novel model that collects both global and local feature mapinformation in the channel and space dimensions of the feature map during thetraining and prediction phases. This approach allows us to successfully extractthe change region's boundary information while also divorcing the changeregion's main body from its boundary. Numerous studies have shown that theassessment metrics and evaluation effects of the model described in this paperon the publicly available datasets DSIFN-CD and S2Looking are the best whencompared to other models.

BD-MSA: Body decouple VHR Remote Sensing Image Change Detection method guided by multi-scale feature information aggregation