Radiometric normalization of overlapping LiDAR intensity data for reduction of striping noise. Issue 7 (2nd July 2016)

• Record Type: Journal Article
• Authors: Yan, Wai Yeung; Shaker, Ahmed
• Is Part Of: International journal of digital earth. Volume 9:Issue 7(2016)
• Page Start: 649
• Page End: 661
• Publication Date: 2016-07-02
• Subjects: LiDAR intensity -- striping noise -- radiometric correction -- radiometric normalization -- joint histogram -- land cover classification; Geographic information systems -- Periodicals; Sustainable development -- Information technology -- Periodicals; Social planning -- Information technology -- Periodicals; 910.285
• DOI: 10.1080/17538947.2015.1111951
• Languages: English
• ISSNs: 1753-8947

Abstract

ABSTRACT: Airborne LiDAR data are usually collected with partially overlapping strips in order to serve a seamless and fine resolution mapping purpose. One of the factors limiting the use of intensity data is the presence of striping noise found in the overlapping region. Though recent researches have proposed physical and empirical approaches for intensity data correction, the effect of striping noise has not yet been resolved. This paper presents a radiometric normalization technique to normalize the intensity data from one data strip to another one with partial overlap. The normalization technique is built based on 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 proposed method was tested with two individual LiDAR datasets collected by Teledyne Optech's Gemini (1064 nm) and Orion (1550 nm) sensors. The experimental results showed that radiometric correction and normalization can significantly reduce the striping noise found in the overlapping LiDAR intensity data and improve its capability in land cover classification. The coefficient of variation of five selected land cover features was reduced by 19–65%, where a 9–18% accuracy improvement was achieved in different classification scenarios. With the proven capability of the proposed method, both radiometric correction and normalization should be applied as a pre-processing step before performing any surface classification and object recognition.