Abstract

Light detection and ranging (LiDAR) intensity data are attracting increasing attention because of the great potential for use of such data in a variety of remote sensing applications. To fully investigate the data potential for target classification and identification, we carried out a series of experiments with typical urban building materials and employed our reconstructed built-in-lab LiDAR system. Received intensity data were analyzed on the basis of the derived bidirectional reflectance distribution function (BRDF) model and the established integration method. With an improved fitting algorithm, parameters involved in the BRDF model can be obtained to depict the surface characteristics. One of these parameters related to surface roughness was converted to a most used roughness parameter, the arithmetical mean deviation of the roughness profile ($R_a$), which can be used to validate the feasibility of the BRDF model in surface characterizations and performance evaluations.

© 2015 Optical Society of America

Surface characteristics modeling and performance evaluation of urban building materials using LiDAR data

Xiaolu Li and Yu Liang
Appl. Opt. 54(15) 4750-4759 (2015)

Bidirectional reflectance distribution function based surface modeling of non-Lambertian using intensity data of light detection and ranging

Xiaolu Li, Yu Liang, and Lijun Xu
J. Opt. Soc. Am. A 31(9) 2055-2063 (2014)

Measurement of roughness based on the Talbot effect in reflection from rough surfaces

Masoomeh Dashtdar, Ali Mohammadzade, and S. Mohammad-Ali Hosseini-Saber
Appl. Opt. 54(16) 5210-5215 (2015)

Width of the specular peak perpendicular to the principal plane for rough surfaces

Gerhard Meister, André Rothkirch, Hartwig Spitzer, and Johann Bienlein
Appl. Opt. 40(33) 6072-6080 (2001)

Modeling and measurements of atomic surface roughness

Yoshiharu Namba, Jin Yu, Jean M. Bennett, and Koujun Yamashita
Appl. Opt. 39(16) 2705-2718 (2000)