An road extraction method for remote sensing image based on Encoder-Decoder network

被引：0

作者：

He H. ^{[1
]}

Wang S. ^{[1
]}

Yang D. ^{[1
]}

Wang S. ^{[1
]}

Liu X. ^{[1
]}

机构：

[1] The Rocket Force University of Engineering, The Department of Control Engineering, Xi'an

[2] The Rocket Force University of Engineering, The Department of Information Engineering, Xi'an

来源：

Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | 2019年 / 48卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Deep learning; Encoder-Decoder network; Remote sensing; Road extraction; Semantic segmentation;

D O I：

10.11947/j.AGCS.2019.20180005

中图分类号：

学科分类号：

摘要：

According to the characteristics of the road features, an Encoder-Decoder deep semantic segmentation network is designed for 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 of representing detail information. Secondly, as the road area is small proportion in remote sensing images, the cross-entropy loss function is improved, which solves the imbalance between positive and negative samples in training process. Experiments on large road extraction dataset 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 road extraction task and needs less artificial participation, so it has a good application prospect. © 2019, Surveying and Mapping Press. All right reserved.

引用

页码：330 / 338

页数：8

共 29 条

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