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Remote Sensing Technology and Application
    
Water Regions Extraction from LiDAR Data in Coast
Liang Qianqian1,3,Zhang Hande1,2,Sun Genyun1,3,Wang Peng1,3
(1.College of Geo-resources and Information,China University of Petroleum (East China),Qingdao 266580,China;
2.Air-Borne Detachment of China Marine Surveillance,Qingdao 266061,China;
3.Laboratory for Marine Mineral Resources,Qingdao National Laboratoryfor Marine Science and Technology,Qingdao 266071,China)
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Abstract  Water regions extraction is of great significance to monitoring,research,planning and development of coastal zones.The conventional method of water regions extraction based on remote sensing image often has poor accuracy in coast because there are large differences among spectral character of different water regions which often change over time as well.To solve this problem,the elevation,intensity and point-density information from the highly accurate 3D mass points of airborne LiDAR was exploited to extract water regions.Firstly,a part of water points were extracted by the characteristics of low density,and then were constrained by the elevation and intensity threshold which came from Statistical table.Secondly,A triangulation network surface model was established based on the water points got from the previous step to describe the elevation trend of water surface.Lastly,all the points which near or behind the surface model was extracted as water points.The result of the accuracy evaluation indicates that the recogniton accuracy of water points is more than 91%.
Key words:  Airborne LiDAR      Water regions extraction      Point cloud      Feature analysis     
Received:  10 January 2017      Published:  16 March 2018
TP 79  
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Liang Qianqian
Zhang Hande
Sun Genyun
Wang Peng

Cite this article: 

Liang Qianqian,Zhang Hande,Sun Genyun,Wang Peng. Water Regions Extraction from LiDAR Data in Coast. Remote Sensing Technology and Application, 2018, 33(1): 136-142.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2018.1.0136     OR     http://www.rsta.ac.cn/EN/Y2018/V33/I1/136

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