Road Extraction from High-Resolution Remote Sensing Images Based on EDRNet Model

doi:10.19678/j.issn.1000-3428.0058876

Computer Engineering ›› 2021, Vol. 47 ›› Issue (9): 297-303,312. doi: 10.19678/j.issn.1000-3428.0058876

• Development Research and Engineering Application • Previous Articles Next Articles

Road Extraction from High-Resolution Remote Sensing Images Based on EDRNet Model

HE Xiaohui¹, LI Daidong², LI Panle², HU Shaokai², CHEN Mingyang², TIAN Zhihui¹, ZHOU Guangsheng³

1. School of Geo-Science and Technology, Zhengzhou University, Zhengzhou 450001, China;
2. School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China;
3. Joint Laboratory of Eco-Meteorology, Chinese Academy of Meteorological Sciences, Zhengzhou University, Zhengzhou 450052, China

Received:2020-07-08 Revised:2020-08-18 Published:2020-08-24

基于EDRNet模型的高分辨率遥感影像道路提取

赫晓慧¹, 李代栋², 李盼乐², 胡绍凯², 陈明扬², 田智慧¹, 周广胜³

1. 郑州大学地球科学与技术学院, 郑州 450001;
2. 郑州大学信息工程学院, 郑州 450001;
3. 中国气象科学研究院郑州大学生态气象联合实验室, 郑州 450052

作者简介:赫晓慧(1978-),女,教授,主研方向为遥感影像处理、数据挖掘;李代栋,硕士研究生;李盼乐,博士;胡绍凯、陈明扬,硕士;田智慧、周广胜,教授、博士。
基金资助:
第二次青藏高原综合科学考察研究项目（2019QZKK0106）。

Abstract

Abstract: The existing methods for extracting the road parts from high-resolution remote sensing images are limited by the incomplete extraction results and poor boundary quality.To address the problem, a new method based on the EDRNet model is proposed for extracting road parts from remote sensing images.The residual network is used to build the road extraction model, EDR1, which retains the detailed information of the road and accelerates the network convergence.Then multi-scale and multi-level road feature information is fused to design a model, EDR2, for optimizing the road extraction results.On this basis, the mixed loss function is designed to make the road extraction results more complete.Experimental results on the Maschusetts Roads dataset show that the recall rate, precision and F1-score of the proposed methods reach 84.4%, 81.7%, and 82.9% respectively.The proposed method can provide complete and accurate extraction results.

Key words: remote sensing image, road extraction, U-Net model, Deep Learning(DL), EDRNet model

摘要： 针对高分辨率遥感影像道路提取结果不完整、边界质量差的问题，提出基于EDRNet模型的遥感影像道路提取方法。利用残差网络构建道路提取模型EDR1，保留道路的细节信息并加速网络收敛。通过融合多尺度、多层次的道路特征信息，设计道路提取结果优化模型EDR2。在此基础上，利用混合损失函数，提高道路提取的完整度。实验结果表明，EDRNet道路提取方法在马萨诸塞州道路数据集上的召回率、精确率和F1-score指标分别达到了84.4%、81.7%及83.0%，其结果完整且准确。

关键词: 遥感影像, 道路提取, U-Net模型, 深度学习, EDRNet模型

CLC Number:

TP391

HE Xiaohui, LI Daidong, LI Panle, HU Shaokai, CHEN Mingyang, TIAN Zhihui, ZHOU Guangsheng. Road Extraction from High-Resolution Remote Sensing Images Based on EDRNet Model[J]. Computer Engineering, 2021, 47(9): 297-303,312.

赫晓慧, 李代栋, 李盼乐, 胡绍凯, 陈明扬, 田智慧, 周广胜. 基于EDRNet模型的高分辨率遥感影像道路提取[J]. 计算机工程, 2021, 47(9): 297-303,312.

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