A Road Extraction Method for Remote Sensing Image Based on Encoder-Decoder Network

查看全文 作　　者：Hao [1]HE;Shuyang [2]WANG;Shicheng [1]WANG;Dongfang [1]YANG;Xing [1]LIU

机构地区：[1]The Rocker Force University of Engineering,The Department of Control Engineering,Xi’an 710025,China;[2]The Rocker Force University of Engineering,The Department of Information Engineering,Xi’an 710025,China

出　　处：《Journal of Geodesy and Geoinformation Science》索引2020年第3卷第2期,共10页

基　　金：National Natural Science Foundation of China(Nos.61673017,61403398);and Natural Science Foundation of Shaanxi Province(Nos.2017JM6077,2018ZDXM-GY-039)。 摘　　要：According to the characteristics of the road features,an Encoder-Decoder deep semantic segmentation network is designed for the road extraction of remote sensing images.Firstly,as the features of the road target are rich in local details and simple in semantic features,an Encoder-Decoder network with shallow layers and high resolution is designed to improve the ability to represent detail information.Secondly,as the road area is a small proportion in remote sensing images,the cross-entropy loss function is improved,which solves the imbalance between positive and negative samples in the training process.Experiments on large road extraction datasets show that the proposed method gets the recall rate 83.9%,precision 82.5%and F1-score 82.9%,which can extract the road targets in remote sensing images completely and accurately.The Encoder-Decoder network designed in this paper performs well in the road extraction task and needs less artificial participation,so it has a good application prospect. 关键词：remote sensing road extraction deep learning semantic segmentation Encoder-Decoder network

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A Road Extraction Method for Remote Sensing Image Based on Encoder-Decoder Network

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