期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2020
卷号：V-3-2020
页码：221-226
DOI：10.5194/isprs-annals-V-3-2020-221-2020
语种：English
出版社：Copernicus Publications
摘要：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 ia/i. Existing literature recommended different ia/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 ia/i value by pairing up the closest data points of two overlapping LiDAR data strips, and subsequently estimating the range normalization parameter ia/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 ia/i value, yielding the lowest icv/i, as verified by a cross validation approach. Nevertheless, the estimated ia/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 ia/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.
