Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/64969
Title: Road network extraction based on airborne lidar and high resolution remote sensing imagery
Other Titles: 基于机载LiDAR和高分辨率遥感影像的城市道路网提取
Authors: Gao, LP
Shi, WZ 
Lv, ZY
Zhang, H
Keywords: Lidar
High resolution remote sensing images
Road network extraction
False road Informa-Tion removing
Mathematical morphology
Image registration
Issue Date: 2013
Publisher: 中国学术期刊 (光盘版) 电子杂志社
Source: 遥感技术与应用 (Remote sensing technology and application), 2013, v. 28, no. 4, p. 562-568 How to cite?
Journal: 遥感技术与应用 (Remote sensing technology and application) 
Abstract: 利用單個數據源的數學形態學道路提取方法不能充分利用道路的特征,提取的道路信息不完整。針對這一缺陷,并考慮到機載LiDAR數據可以提供高程信息,提出了將機載LiDAR數據和高分辨率遙感影像數據結合起來的城市道路網的提取方法。以徐州市的機載LiDAR數據和高分辨率遙感影像數據作為實驗數據,首先將兩者進行精確配準,然后利用偽道路信息去除的方法分別將植被信息和建筑物信息等去除,得到基本的道路輪廓,再利用形態細化算法提取道路的中心線,最后,在ArcGIS和Matlab編程環境下實現了改進的道路修剪算法(IRT),利用該算法進行道路修剪,得到了平滑和連貫的城市道路網。經過精度評價可以看出:利用該方法可以較好地避免建筑物陰影、低矮植被群等對道路提取的影響,道路的識別精度達到84%以上。
The conventional mathematical morphology method using single data source to extract road net work which could not take full advantage of the road characteristics, the extracted road information was not complete. In view of this drawback, and base on the airborne LiDAR data can provide elevation informa tion,this paper proposes a method which combines the airborne LiDAR data with high resolution remote sensing images to extract city road network. The airborne LiDAR data and high resolution remote sensing QuickBird images of Xuzhou were taken as the experimental data, the precise registration between them were first done, then the FRIR (False Road Information Removing) method was used to remove the vegeta tion and buildings separately,so the basic road contour was displayed. Finally, this paper achieved an Im- proved Road Trimming (IRT) algorithm under the ArcGIS and Matlab programming environment, the road network was trimmed by the algorithm, then a smooth and continuous city road network was obtained. The result of the accuracy evaluation indicates that the method was proposed can be used to avoid the influence of the building shadow, city squares, parking lots and the vegetation groups on both sides of the road to the road centerlines extraction well, and the recognition accuracy of the road network is more than 84 %.
URI: http://hdl.handle.net/10397/64969
ISSN: 1004-0323
Rights: © 2013 中国学术期刊电子杂志出版社。本内容的使用仅限于教育、科研之目的。
© 2013 China Academic Journal Electronic Publishing House. It is to be used strictly for educational and research purposes.
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