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

Area of Science:

• Remote Sensing
• Geophysics
• Material Science

Background:

• Light Detection and Ranging (LiDAR) intensity data analysis is crucial for extracting surface features.
• Target classification using LiDAR data is a significant area of remote sensing research.
• Existing methods require improved models for accurate characterization of diverse surfaces.

Purpose of the Study:

• To derive a new bidirectional reflectance distribution function (BRDF) model for characterizing rough and smooth surfaces.
• To improve the integration method for relating the BRDF model to LiDAR intensity data.
• To validate the proposed model and method using typical urban building materials.

Main Methods:

• Development of a new BRDF model tailored for LiDAR system geometry.
• Coordinate transformation for an improved integration method linking BRDF and LiDAR intensity.
• Experimental validation using various urban building materials.

Main Results:

• The proposed BRDF model accurately fits experimental data for urban building materials.
• Three extracted BRDF parameters effectively distinguish materials based on roughness and reflectance properties.
• The model demonstrates potential for physically rigorous surface feature characterization.

Conclusions:

• The new BRDF model and integration method provide a robust approach for LiDAR intensity data analysis.
• The model's parameters offer a reliable basis for classifying urban building materials.
• This research advances the application of LiDAR in detailed surface feature extraction and target identification.