Abstract
Abstract. To serve seamless mapping, airborne LiDAR data are usually collected with multiple parallel strips with one or two cross strip(s). Nevertheless, the overlapping regions of LiDAR data strips are usually found with unbalanced intensity values, resulting in the appearance of stripping noise. Despite that physical intensity correction methods are recently proposed, some of the system and environmental parameters are assumed as constant or not disclosed, leading to such an intensity discrepancy. This paper presents a new normalization technique to adjust the radiometric misalignment found in the overlapping LiDAR data strips. The normalization technique is built upon a second-order polynomial function fitted on the joint histogram plot, which is generated with a set of pairwise closest data points identified within the overlapping region. The method was tested on Teledyne Optech’s Gemini dataset (at 1064 nm wavelength), where the LiDAR intensity data were first radiometrically corrected based on the radar (range) equation. Five land cover features were selected to evaluate the coefficient of variation (cv) of the intensity values before and after implementing the proposed method. Reduction of cv was found by 19% to 59% in the Gemini dataset, where the striping noise was significantly reduced in the radiometrically corrected and normalized intensity data. The Gemini dataset was also used to conduct land cover classification, and the overall accuracy yielded a notable improvement of 9% to 18%. As a result, LiDAR intensity data should be pre-processed with radiometric correction and normalization prior to any data manipulation.
Highlights
The use of airborne LiDAR data has progressively increased for surface classification and object recognition (Yan et al, 2015)
This paper presents a radiometric normalization technique to reduce the striping noise appeared in the overlapping region of airborne LiDAR intensity data strips
The normalization model is built upon the use of a 2nd order polynomial function fitted on a joint histogram plot, which is generated based on a set of pairwise intensity data points identified within the overlapping LiDAR data strips
Summary
The use of airborne LiDAR data has progressively increased for surface classification and object recognition (Yan et al, 2015). Though various correction and calibration techniques have been proposed to reduce the intensity discrepancy based on the use of radar (range) equation (Hofle and Pfeifer, 2007; Kaasalainen et al, 2009; Wagner, 2010; Yan et al, 2012), only a few studies address the striping noise issue when dealing with the overlapping LiDAR data strips. Luzum et al (2004) proposed a method to normalize the observed LiDAR intensity by multiplying a dynamic range factor to the power of f (f = 2), where such dynamic range factor equals to the range of the observed point divided by a standard range Such dynamic range normalization method has been enhanced and used to normalize multiple overlapping LiDAR data strips, for forest canopies, with a notable improvement in terms of classification accuracy (Korpela et al, 2010a,b; Gatziolis, 2011). Though there exists preliminary attempts to incorporate Phong model in the radar (range) equation for overlap data strip correction (Ding et al, 2013), Jutzi and Gross (2010) addressed that the Phong model does not really outperform the traditional Lambertian assumption in terms of intensity homogeneity
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