Title :
Classification of Lidar Data Using Standard Deviation of Elevation and Characteristic Point Features
Author :
Amolins, Krista ; Zhang, Yun ; Dare, Peter
Author_Institution :
Dept. of Geodesy & Geomatics Eng., Univ. of New Brunswick, NB
Abstract :
A simple classification scheme is proposed for LiDAR data from a mixed urban area. The basic classifications are urban, low, high, and other vegetation, and water. Standard deviation of elevation within a grid cell, point return number, number of returns per pulse, and point return intensity are used to classify each point individually. Additional classifications are based on the average elevation of the basic classes. The scheme classifies up to three-quarters of data points.
Keywords :
geophysical techniques; image classification; optical radar; topography (Earth); vegetation; LiDAR data classification scheme; elevation; grid cell; mixed urban area; point features; point return intensity; point return number; standard deviation; vegetation; water; Clouds; Data engineering; Data mining; Geodesy; Laser modes; Laser radar; Optical pulse generation; Optical pulses; Urban areas; Vegetation mapping; LiDAR; classification;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4244-2807-6
Electronic_ISBN :
978-1-4244-2808-3
DOI :
10.1109/IGARSS.2008.4779133
