Accession Number:



Spectral Lidar Analysis and Terrain Classification in a Semi-Urban Environment

Descriptive Note:

[Technical Report, Master's Thesis]

Corporate Author:

Naval Postgraduate School

Personal Author(s):

Report Date:


Pagination or Media Count:



Remote-sensing analysis is conducted for the Naval Postgraduate School campus, containing buildings, impervious surfaces asphalt and concrete, natural ground, and vegetation. Data is from the Optech Titan, providing three-wavelength laser data 532, 1064, and 1550 nm at 1015 pointsmsup2. Analysis techniques for laser-scanner LiDAR data traditionally use only x, y, z coordinate information. The traditional approach is used to initialize the classification process into broad-spatial classes unclassified, ground, vegetation, and buildings.Spectral analysis contributes a unique approach to the classification process. Tools and techniques designed for multispectral imagery are adapted to the LiDAR analysis herein. ENVIs N-Dimensional Visualizer is employed to develop training sets for supervised classification techniques, primarily Maximum Likelihood. Unsupervised classification for the combined spatialspectral data is accomplished using a K-means classifier for comparison. The campus is classified into 10 and 16 classes, compared to the four from traditional methods. Addition of the spectral component improves the discrimination among impervious surfaces, other ground elements, and building materials. Maximum Likelihood demonstrates 75 percent overall classification accuracy, with grass 99.9 percent, turf 95 percent, asphalt shingles 94 percent, light-building concrete 89 percent, sand 88 percent, shrubs 85 percent, asphalt 84 percent, trees 80 percent, and clay-tile shingles 77 percent. Post-process filtering by number of returns increases overall accuracy to 82 percent.


Subject Categories:

  • Target Direction, Range and Position Finding
  • Optical Detection and Detectors

Distribution Statement:

[A, Approved For Public Release]