Road extraction from multi-source high resolution remote sensing image based on fully convolutional neural network

doi:10.11772/j.issn.1001-9081.2017122923

Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (7): 2070-2075.DOI: 10.11772/j.issn.1001-9081.2017122923

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Road extraction from multi-source high resolution remote sensing image based on fully convolutional neural network

ZHANG Yonghong¹, XIA Guanghao¹, KAN Xi², HE Jing¹, GE Taotao¹, WANG Jiangeng²

1. School of Information and Control, Nanjing University of Information Science & Technology, Nanjing Jiangsu 210044, China;
2. School of Atmospheric Science, Nanjing University of Information Science & Technology, Nanjing Jiangsu 210044, China

Received:2017-12-14 Revised:2018-02-05 Online:2018-07-10 Published:2018-07-12
Supported by:
This work is partially supported by International (Regional) Cooperation and Exchange Project of the National Natural Science Foundation of China (41661144039).

基于全卷积神经网络的多源高分辨率遥感道路提取

张永宏¹, 夏广浩¹, 阚希², 何静¹, 葛涛涛¹, 王剑庚²

1. 南京信息工程大学信息与控制学院, 南京 210044;
2. 南京信息工程大学大气科学学院, 南京 210044

通讯作者: 夏广浩
作者简介:张永宏(1974-),男,山东临沂人,教授,博士生导师,博士,主要研究方向:模式识别、智能系统、图像识别检测;夏广浩(1994-),男,江苏宿迁人,硕士研究生,主要研究方向:图像处理、机器学习;阚希(1989-),男,江苏无锡人,博士研究生,主要研究方向:遥感图像处理、机器学习;何静(1994-),女,江苏泰州人,硕士研究生,主要研究方向:山地灾害数据库;葛涛涛(1994-),男,江苏扬州人,硕士研究生,主要研究方向:灾害预警、机器学习;王剑庚(1982-),男,甘肃定西人,讲师,博士,主要研究方向:遥感图像处理。
基金资助:
国家自然科学基金国际（地区）合作与交流项目（41661144039）。

Abstract

Abstract: The semi-automatic road extraction method needs more artificial participation and is time-consuming, and its accuracy of road extraction is low. In order to solve the problems, a new method of road extraction from multi-source high resolution remote sensing image based on Fully Convolutional neural Network (FCN) was proposed. Firstly, the GF-2 and World View high resolution remote sensing images were divided into small pieces, the images containing roads were classified by Convolutional Neural Network (CNN). Then, the Canny operator was used to extract the edge feature information of road. Finally, RGB, Gray and ground truth were combined and put into the FCN model for training, and the existing FCN model was extended to a new FCN model with multi-satellite source input and multi-feature source input. The Shigatse region of Tibet was chosen as the research area. The experimental results show that, the proposed method can achieve the extraction precision of 99.2% in the road extraction from high resolution remote sensing images, and effectively reduce the time needed for extraction.

Key words: Fully Convolutional neural Network (FCN), multi-source input, remote sensing image, road extraction, Canny operator

摘要： 针对半自动道路提取方法人工参与较多、提取精度不高且较为耗时的问题提出一种基于全卷积神经网络（FCN）的多源高分辨率遥感道路提取方法。首先，对高分二号和World View图像进行分割，用卷积神经网络（CNN）分类出包含道路的图像；然后，用Canny算子提取道路的边缘特征信息；最后，结合RGB、Gray和标签图放入FCN中训练，将现有的FCN模型拓展为多卫星源输入及多特征源输入的FCN模型。选取西藏日喀则地区作为研究区域，实验结果显示，所提方法在对高分辨率遥感影像进行道路提取时能够达到99.2%的提取精度，并且有效地减少了提取所需的时间。

关键词: 全卷积神经网络, 多源输入, 遥感图像, 道路提取, Canny算子

CLC Number:

TP751

ZHANG Yonghong, XIA Guanghao, KAN Xi, HE Jing, GE Taotao, WANG Jiangeng. Road extraction from multi-source high resolution remote sensing image based on fully convolutional neural network[J]. Journal of Computer Applications, 2018, 38(7): 2070-2075.

张永宏, 夏广浩, 阚希, 何静, 葛涛涛, 王剑庚. 基于全卷积神经网络的多源高分辨率遥感道路提取[J]. 计算机应用, 2018, 38(7): 2070-2075.

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Road extraction from multi-source high resolution remote sensing image based on fully convolutional neural network

基于全卷积神经网络的多源高分辨率遥感道路提取

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