High-resolution Image Building Target Detection Based on Edge Feature Fusion

Abstract

High-resolution remote sensing image building target detection has a wide range of application value in territorial planning,geographic monitoring,smart city and other fields.However,due to the complex background of remote sensing images,some detailed features of building targets are less distinguishable from the background.During the task,it is prone to problems such as distortion and missing of the building outline.Aiming at this problem,an adaptive weighted edge feature fusion network (VAF-Net) is designed.This method is aimed at remote sensing image building detection tasks,expands the classic codec network U-Net network,and makes up for the lack of detailed features in basic network learning through the fusion of RGB feature maps and edge feature maps.At the same time,relying on network learning to automatically update the fusion weight,adaptive weighted fusion can be achieved,and the complementary information of different features can be full made use of.This method is tested on the Massachusetts Buildingsdata set,and its accuracy,recall and F1-score reach 82.1%,82.5% and 82.3%,respectively.The comprehensive index F1-score increases by about 6% compared to the basic network.VAF-Net effectively improves the perfor-mance of the codec network for high-resolution image building target detection tasks,and has good practical value.

Keywords

• target detection|feature fusion|neural network|edge feature|u-net