Open AccessREDUCTION OF STRIPING NOISE IN OVERLAPPING LIDAR INTENSITY DATA BY RADIOMETRIC NORMALIZATION
Author(s) -
Wai Yeung Yan,
Ahmed Shaker
Publication year - 2016
Publication title -
the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.264
H-Index - 71
eISSN - 1682-1777
pISSN - 1682-1750
DOI - 10.5194/isprsarchives-xli-b1-151-2016
Subject(s) - lidar , normalization (sociology) , strips , remote sensing , histogram , intensity (physics) , environmental science , mathematics , computer science , optics , artificial intelligence , geography , physics , algorithm , sociology , anthropology , image (mathematics)
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 (<i>cv</i>) of the intensity values before and after implementing the proposed method. Reduction of <i>cv</i> 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.