Extraction and Optimization of Rural Road Vector Centerline from Remote Sensing Images Based on Improved U-Net Network
Accurate extraction of rural road vector centerlines from remote sensing images is of great significance to rural plan-ning and the construction of geographic information database.The existing image segmentation algorithms,such as U-Net net-work,can not effectively solve the influence of background information such as terrain shadows and trees on road extraction,so this paper improves the U-Net network to improve the accuracy of segmentation results.Firstly,the designed network structure is downsampled twice,and the two information features in the context are connected by a jump layer,so that the output road has clear detail expression ability.Secondly,in order to reduce the influence of poor training effect caused by unbalanced samples,this paper adopts the superposition of cross entropy loss function and generalized dice loss function to improve the training effect.Finally,in order to get the high-precision rural road vector centerline,the existing neighborhood centroid voting algorithm and fusion algorithm are used to vectorize and optimize the extracted road centerline.The experimental results show that the im-proved U-Net network combined with fusion algorithm is feasible in extracting the centerline of rural road vector,with an accu-racy rate of 95.03％,which is obviously improved compared with the existing algorithms.The algorithm proposed in this paper provides a new idea and method for high-precision extraction of rural road vector centerline from remote sensing images.
improved U-Net networkremote sensing imagenetwork segmentationrural road extractionvector line optimization
万方数据
维普
