ESTIMATION OF OPTIMAL PARAMETER FOR RANGE NORMALIZATION OF MULTISPECTRAL AIRBORNE LIDAR INTENSITY DATA

Range normalization is a common data pre-process that aims to improve the radiometric quality of airborne LiDAR data. This radiometric treatment considers the rate of energy attenuation sustained by the laser pulse as it travels through a medium back and forth from the LiDAR system to the surveyed o...

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Main Authors: M. H. Kwan, W. Y. Yan
Format: Article
Language:English
Published: Copernicus Publications 2020-08-01
Series:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2020/221/2020/isprs-annals-V-3-2020-221-2020.pdf
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author M. H. Kwan
W. Y. Yan
W. Y. Yan
author_facet M. H. Kwan
W. Y. Yan
W. Y. Yan
author_sort M. H. Kwan
collection DOAJ
description Range normalization is a common data pre-process that aims to improve the radiometric quality of airborne LiDAR data. This radiometric treatment considers the rate of energy attenuation sustained by the laser pulse as it travels through a medium back and forth from the LiDAR system to the surveyed object. As a result, the range normalized intensity is proportional to the range to the power of a factor <i>a</i>. Existing literature recommended different <i>a</i> values on different land cover types, which are commonly adopted in forestry studies. Nevertheless, there is a lack of study evaluating the range normalization on multispectral airborne LiDAR intensity data. In this paper, we propose an overlap-driven approach that is able to estimate the optimal <i>a</i> value by pairing up the closest data points of two overlapping LiDAR data strips, and subsequently estimating the range normalization parameter <i>a</i> based on a least-squares adjustment. We implemented the proposed method on a set of multispectral airborne LiDAR data collected by a Optech Titan, and assessed the coefficient of variation of four land cover types before and after applying the proposed range normalization. The results showed that the proposed method was able to estimate the optimal <i>a</i> value, yielding the lowest <i>cv</i>, as verified by a cross validation approach. Nevertheless, the estimated <i>a</i> value is never identical for the four land cover classes and the three laser wavelengths. Therefore, it is not recommended to label a specific <i>a</i> value for the range normalization of airborne LiDAR intensity data within a specific land cover type. Instead, the range normalization parameter is deemed to be data-driven and should be estimated for each LiDAR dataset and study area.
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spelling doaj.art-1f2ac5ce2f8c43e4bd54d5b6551631ce2022-12-21T17:50:14ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502020-08-01V-3-202022122610.5194/isprs-annals-V-3-2020-221-2020ESTIMATION OF OPTIMAL PARAMETER FOR RANGE NORMALIZATION OF MULTISPECTRAL AIRBORNE LIDAR INTENSITY DATAM. H. Kwan0W. Y. Yan1W. Y. Yan2Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hong KongDepartment of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hong KongDepartment of Civil Engineering, Ryerson University, Toronto, Ontario, CanadaRange normalization is a common data pre-process that aims to improve the radiometric quality of airborne LiDAR data. This radiometric treatment considers the rate of energy attenuation sustained by the laser pulse as it travels through a medium back and forth from the LiDAR system to the surveyed object. As a result, the range normalized intensity is proportional to the range to the power of a factor <i>a</i>. Existing literature recommended different <i>a</i> values on different land cover types, which are commonly adopted in forestry studies. Nevertheless, there is a lack of study evaluating the range normalization on multispectral airborne LiDAR intensity data. In this paper, we propose an overlap-driven approach that is able to estimate the optimal <i>a</i> value by pairing up the closest data points of two overlapping LiDAR data strips, and subsequently estimating the range normalization parameter <i>a</i> based on a least-squares adjustment. We implemented the proposed method on a set of multispectral airborne LiDAR data collected by a Optech Titan, and assessed the coefficient of variation of four land cover types before and after applying the proposed range normalization. The results showed that the proposed method was able to estimate the optimal <i>a</i> value, yielding the lowest <i>cv</i>, as verified by a cross validation approach. Nevertheless, the estimated <i>a</i> value is never identical for the four land cover classes and the three laser wavelengths. Therefore, it is not recommended to label a specific <i>a</i> value for the range normalization of airborne LiDAR intensity data within a specific land cover type. Instead, the range normalization parameter is deemed to be data-driven and should be estimated for each LiDAR dataset and study area.https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2020/221/2020/isprs-annals-V-3-2020-221-2020.pdf
spellingShingle M. H. Kwan
W. Y. Yan
W. Y. Yan
ESTIMATION OF OPTIMAL PARAMETER FOR RANGE NORMALIZATION OF MULTISPECTRAL AIRBORNE LIDAR INTENSITY DATA
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
title ESTIMATION OF OPTIMAL PARAMETER FOR RANGE NORMALIZATION OF MULTISPECTRAL AIRBORNE LIDAR INTENSITY DATA
title_full ESTIMATION OF OPTIMAL PARAMETER FOR RANGE NORMALIZATION OF MULTISPECTRAL AIRBORNE LIDAR INTENSITY DATA
title_fullStr ESTIMATION OF OPTIMAL PARAMETER FOR RANGE NORMALIZATION OF MULTISPECTRAL AIRBORNE LIDAR INTENSITY DATA
title_full_unstemmed ESTIMATION OF OPTIMAL PARAMETER FOR RANGE NORMALIZATION OF MULTISPECTRAL AIRBORNE LIDAR INTENSITY DATA
title_short ESTIMATION OF OPTIMAL PARAMETER FOR RANGE NORMALIZATION OF MULTISPECTRAL AIRBORNE LIDAR INTENSITY DATA
title_sort estimation of optimal parameter for range normalization of multispectral airborne lidar intensity data
url https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2020/221/2020/isprs-annals-V-3-2020-221-2020.pdf
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AT wyyan estimationofoptimalparameterforrangenormalizationofmultispectralairbornelidarintensitydata
AT wyyan estimationofoptimalparameterforrangenormalizationofmultispectralairbornelidarintensitydata