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Proceedings Paper

Analysis and exploitation of lidar waveform data
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Paper Abstract

Data from the Optech Titan airborne laser scanner were collected over Monterey, CA, in three wavelengths (532 nm, 1064 nm, and 1550 nm), in October 2016, by the National Center for Airborne LiDAR Mapping (NCALM). Lidar waveform data at 532 nm from the Optech Titan were analyzed for data collected over the forested area at the Pont Lobos State Park. Standard point cloud processing was done using LAStools. Waveform data were converted into pseudo “hypercubes” in order to facilitate use of the analysis structures used for hyperspectral imagery. Analysis approaches used were ENVI classification tools such as Support Vector Machines (SVM), Spectral Angle Mapper (SAM), Maximum Likelihood, and K-means to classify returns. Through the use of this analog to hyperspectral data analysis to classify vegetation and terrain, the results are that, by using the Support Vector Machines with full waveform data, we can successfully improve low vegetation classifiers by 40%, and differentiate tree types (Pine/Cypress) at 40–60% accuracy.

Paper Details

Date Published: 2 May 2019
PDF: 10 pages
Proc. SPIE 11005, Laser Radar Technology and Applications XXIV, 110050M (2 May 2019); doi: 10.1117/12.2519188
Show Author Affiliations
Richard C. Olsen, Naval Postgraduate School (United States)
Andrew S. Davis, Naval Postgraduate School (United States)
Jeremy P. Metcalf, Naval Postgraduate School (United States)


Published in SPIE Proceedings Vol. 11005:
Laser Radar Technology and Applications XXIV
Monte D. Turner; Gary W. Kamerman, Editor(s)

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