Road extraction technology based on multi-source remote sensing data: review and prospects

被引：0
|
作者：
Jia J.-X. [1 ]
Sun H.-B. [2 ]
Jiang C.-H. [3 ]
Wang Y.-M. [4 ]
Wang T.-H. [5 ]
Chen J.-S. [1 ]
Chen Y.-W. [3 ]
机构：
[1] Center for Geo-Spatial Information, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen
[2] Key Laboratory of Intelligent Infrared Perception, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai
[3] Department of Photogrammetry and Remote Sensing, Finnish Geospatial Research Institute, Krikkonummi
[4] Key Laboratory of Space Active Opto-Electronics Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai
[5] Huawei Helsinki Research Center, Helsinki
来源：
Guangxue Jingmi Gongcheng/Optics and Precision Engineering | 2021年 / 29卷 / 02期
关键词：
Hyperspectral; Multi-source remote sensing data; New infrastructure; Road extraction;
D O I：
10.37188/OPE.20212902.0430
中图分类号：
学科分类号：
摘要：
Extracting road information from remote sensing (RS) images is a necessary step in traditional RS applications, such as detecting land usage and updating geographic information systems. It is also critical for new infrastructures, such as digital cities and intelligent transportation. Based on their development processes and the different data sources, existing road extraction methods for RS images can mainly be divided into high-resolution imaging, multispectral/hyperspectral imaging, laser/point cloud imaging, and synthetic aperture radar (SAR) imaging. In this review, we introduce the application status, applicable scope, and method characteristics of four RS technologies. Additionally, we analyze the development and current application effects of road-based methods on hyperspectral data from different platforms. Finally, we summarize the content of this paper and discuss future development trends. © 2021, Science Press. All right reserved.
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页码：430 / 442
页数：12
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