@inproceedings{1443a7484816484f968ac6326ebbe1ef,
title = "Estimation of optimal parameter for range normalization of multispectral airborne LiDAR intensity data",
abstract = "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 a. Existing literature recommended different a 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 a value by pairing up the closest data points of two overlapping LiDAR data strips, and subsequently estimating the range normalization parameter a 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 a value, yielding the lowest cv, as verified by a cross validation approach. Nevertheless, the estimated a value is never identical for the four land cover classes and the three laser wavelengths. Therefore, it is not recommended to label a specific a 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.",
keywords = "LiDAR Intensity, Multispectral LiDAR, Optech Titan, Radiometric Correction, Range Normalization",
author = "Kwan, {Man Ho} and Yan, {Wai Yeung}",
note = "Funding Information: The research was supported by the Hong Kong Polytechnic University FCE start-up fund (BE2U). The multispectral LiDAR dataset was obtained through the AWARE project (NSERC File: CRDPJ 462973 - 14, Grantee: N.C. Coops, FRM, UBC), in collaboration with Canadian Wood Fiber Centre (CWFC), FP-Innovations and Tembec. The authors would like to express appreciation to Dr. Ahmed Shaker at Ryerson University, and Dr. Karin van Ewijk and Prof. Paul Treitz at Queen{\textquoteright}s University for providing the dataset, technical support and illuminating discussions on the research problem. Publisher Copyright: {\textcopyright} Authors 2020. All rights reserved.",
year = "2020",
month = aug,
day = "3",
doi = "https://doi.org/10.5194/isprs-annals-V-3-2020-221-2020",
language = "English",
volume = "5",
series = "ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences",
publisher = "Copernicus GmbH",
pages = "221--226",
booktitle = "ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences",
edition = "3",
}