Machine Learning-Based Material Classification on Spectral Data for a New Multispectral LiDAR Design
This study presents a machine learning framework for material classification using multispectral LiDAR reflectivity data. The classification results support the prototyping of a Raman-source multispectral LiDAR operating within high atmospheric transmission windows, enabling real-time range and spectral measurements to enhance material differentiation and object classification.
Authors
- Trevor L. Courtney (ORCID: https://orcid.org/0000-0002-0329-8071)
- Jarrod P. Brown (ORCID: https://orcid.org/0000-0003-2813-5233)
- Darrell Card
- Richard K. Martin (ORCID: https://orcid.org/0000-0003-1104-0414)
- A. Noble
- William Collins (ORCID: https://orcid.org/0000-0002-2835-6389)
- Daniel E. Leaird (ORCID: https://orcid.org/0000-0003-3913-4754)
- Christian Keyser (ORCID: https://orcid.org/0000-0001-9540-7919)
- Daniel Santana de Carvalho (ORCID: https://orcid.org/0000-0001-6590-4287)
- Arielle Adams (ORCID: https://orcid.org/0000-0003-1024-9566)
Institutions
- United States Air Force Research Laboratory (US)
- Patrick Air Force Base (US)
- Torch Technologies (United States) (US)
- Emerald Coast Science Center (US)
- United States Air Force (US)
- U.S. Air Force Institute of Technology (US)
- University of West Florida (US)
Publication Details
- Published
- 2025-08-13
- DOI
- https://doi.org/10.1109/rapid64712.2025.11151336
- Primary Topic
- 3D Surveying and Cultural Heritage
- Type
- article
- Field-Weighted Citation Impact
- 0.00