BD-MSA: Body Decouple VHR Remote Sensing Image Change Detection Method Guided by Multiscale Feature Information Aggregation

Abstract

The purpose of remote sensing image change detection (RSCD) is to detect differences between bitemporal 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 multiscale by feature aggregation change detection, 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 article on the publicly available datasets DSIFN-CD, S2Looking, and WHU-CD are the best when compared to other models.

Keywords

• Body decouple
• change detection (CD)
• multiscale information aggregation
• very-high-resolution (VHR) images