Computer Engineering and Applications ›› 2018, Vol. 54 ›› Issue (13): 1-10.DOI: 10.3778/j.issn.1002-8331.1804-0271

Previous Articles     Next Articles

Summary of road extraction methods for remote sensing images

ZHANG Yonghong, HE Jing, KAN Xi, XIA Guanghao, ZHU Linglong, GE Taotao   

  1. School of Information and Control, Nanjing University of Information Science & Technology, Nanjing 210044, China
  • Online:2018-07-01 Published:2018-07-17


张永宏,何  静,阚  希,夏广浩,朱灵龙,葛涛涛   

  1. 南京信息工程大学 信息与控制学院,南京 210044

Abstract: Road information plays an important role in modern society. It is of great scientific significance to study the road extraction method of remote sensing image. This paper reviews the development process of road extraction method, and divides the existing road extraction methods into three categories: based on pixel, object-oriented and deep learning according to the realization form. It is used as a clue to analyze and compare the scope of application of various methods with advantages and disadvantages. Design experiments, with a number of high-resolution satellite remote sensing images as the experimental object, verify the comparison of various types of typical road extraction method of the actual performance. The experimental results show that the method of road extraction based on deep learning is the best. Finally, based on the theory of popular remote sensing data and artificial intelligence, the development trend of road extraction method of remote sensing image is prospected.

Key words: satellite remote sensing, image processing, road extraction, deep learning

摘要: 道路信息在现代社会中扮演着重要的角色,研究遥感图像的道路提取方法具有重要科学意义。回顾了道路提取方法的发展历程,按实现形式的不同,将已有道路提取方法分为基于像元、面向对象、深度学习三大类,并以此为线索,分析比较各类方法的适用范围与优缺点。设计实验,以多幅高分辨率卫星遥感图像为实验对象,验证对比各类典型道路提取方法的实际性能,实验结果表明,基于深度学习的道路提取方法效果最佳。最后,结合当下热门的遥感大数据与人工智能相关理论,展望了未来遥感图像道路提取方法的发展趋势。

关键词: 卫星遥感, 图像处理, 道路提取, 深度学习