VHR imagery change detection using UNet based on fusion of residual polymer and attention mechanism

Abstract

VHR imagery change detection is one of research hotspots and difficulties in the field of remote sensing. However, the traditional remote sensing image change detection method is a waste of time and energy and low efficiency. In recent years, deep learning approaches in remote sensing image change detection verified feasible and save time to improve efficiency. A UNet change detection method based on aggregation residuals and attention mechanism is proposed, using prior knowledge of deep learning. The UNet model is used as the basic model, and the aggregation residual module is introduced in the up-down sampling stage, which can fully extract the feature information of the image. The weight of each component in the feature graph can be adjusted by adding attention module in the jump connection layer. In the process of experiment based on the model parameters are reasonable and effective set of data sets to Longnan remote sensing image change detection, and the experimental results showing that compared with the traditional deep learning semantic segmentation method, this article methods F1 value of 0.873, the generated change detection figure closer to label figure, higher accuracy, shorter.