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Bidirectional reflectance distribution function based surface modeling of non-Lambertian using intensity data of
Summary
This study models light detection and ranging (LiDAR) target classification by linking LiDAR intensity data with the bidirectional reflectance distribution function (BRDF). New parameters effectively describe surface characteristics for improved classification.
Area of Science:
- Remote Sensing
- Optical Physics
Background:
- Accurate light detection and ranging (LiDAR) target classification is crucial.
- Understanding the relationship between LiDAR intensity data and surface properties is key.
Purpose of the Study:
- To develop a credible model for LiDAR target classification.
- To investigate the link between LiDAR intensity and the bidirectional reflectance distribution function (BRDF).
Main Methods:
- An integration method using a coaxial laser detection system was developed.
- An intermediary BRDF model (Schlick's) was incorporated to account for diffuse and specular backscattering.
- Measurement campaigns analyzed the influence of incident angle and detection range on intensity data.
Main Results:
- Two parameters, r and S(λ), were extracted, reflecting surface features related to energy distribution and magnitude.
- These parameters demonstrate sensitivity to different surface characteristics.
Conclusions:
- The developed model provides a plausible method for describing surface characteristics.
- The combination of parameters r and S(λ) enhances LiDAR target classification accuracy.
