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

Volume

2

fYear

2008

fDate

7-11 July 2008

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/IGARSS.2008.4779133

Filename

4779133