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Automatic Classification of Coarse Density Lidar Data in Urban Area : Volume Xl-5, Issue 1 (05/06/2014)

By Badawy, H.M.

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Book Id: WPLBN0004015270
Format Type: PDF Article :
File Size: Pages 5
Reproduction Date: 2015

Title: Automatic Classification of Coarse Density Lidar Data in Urban Area : Volume Xl-5, Issue 1 (05/06/2014)  
Author: Badawy, H.M.
Volume: Vol. XL-5, Issue 1
Language: English
Subject: Science, Isprs, International
Collections: Periodicals: Journal and Magazine Collection, Copernicus Publications
Historic
Publication Date:
2014
Publisher: Copernicus Publications, Göttingen, Germany
Member Page: Copernicus Publications

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Moussa, A., El-Sheimy, N., & Badawy, H. (2014). Automatic Classification of Coarse Density Lidar Data in Urban Area : Volume Xl-5, Issue 1 (05/06/2014). Retrieved from http://community.worldlibrary.net/


Description
Description: Department of Geomatics Engineering, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada. The classification of different objects in the urban area using airborne LIDAR point clouds is a challenging problem especially with low density data. This problem is even more complicated if RGB information is not available with the point clouds. The aim of this paper is to present a framework for the classification of the low density LIDAR data in urban area with the objective to identify buildings, vehicles, trees and roads, without the use of RGB information. The approach is based on several steps, from the extraction of above the ground objects, classification using PCA, computing the NDSM and intensity analysis, for which a correction strategy was developed. The airborne LIDAR data used to test the research framework are of low density (1.41 pts/m2) and were taken over an urban area in San Diego, California, USA. The results showed that the proposed framework is efficient and robust for the classification of objects.

Summary
Automatic Classification of coarse density LiDAR data in urban area

 

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