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

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Spectral Lidar Analysis and Terrain Classification in a Semi-Urban Environment NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS Spectral LiDAR Analysis and Terrain Classification in a Semi-Urban Environment by Charles A. McIver March 2017 Thesis Advisor: Richard Olsen Co-Advisor: Marcus Stefanou Approved for public release. Distribution is unlimited. THIS PAGE INTENTIONALLY LEFT BLANK REPORT DOCUMENTATION PAGE Form Approved OMB No. 0704–0188 Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington, DC 20503. 1. AGENCY USE ONLY 2. REPORT DATE 3. REPORT TYPE AND DATES COVERED (Leave blank) March 2017 Master’s Thesis 4. TITLE AND SUBTITLE 5. FUNDING NUMBERS Spectral LiDAR Analysis and Terrain Classification in a Semi-Urban Environment 6. AUTHOR(S) Charles A. McIver 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING Naval Postgraduate School ORGANIZATION REPORT Monterey, CA 93943-5000 NUMBER 9. SPONSORING/MONITORING AGENCY NAME(S) AND 10. SPONSORING/ ADDRESS(ES) MONITORING AGENCY N/A REPORT NUMBER 11. SUPPLEMENTARY NOTES The views expressed in this thesis are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government. IRB Protocol number ____N/A____. 12a. DISTRIBUTION/AVAILABILITY STATEMENT 12b. DISTRIBUTION CODE Approved for public release. Distribution is unlimited. 13. ABSTRACT (maximum 200 words) Remote-sensing analysis is conducted for the Naval Postgraduate School campus, containing buildings, impervious surfaces (asphalt and concrete), natural ground, and vegetation. Data are from the Optech Titan scanner, providing three-wavelength laser data (532, 1064, and 1550 nm) at 10-15 points/m2. 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. ENVI’s N-Dimensional Visualizer is employed to develop training sets for supervised classification techniques, primarily Maximum Likelihood. Unsupervised classification for the combined spatial/spectral data is accomplished using a K-means classifier for comparison. The campus is classified into 10-16 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% overall classification accuracy, with grass (99.9%), turf (95%), asphalt shingles (94%), light-building concrete (89%), sand (88%), shrubs (85%), asphalt (84%), trees (80%), and clay-tile shingles (77%). Post-process filtering by ‘number of returns’ increases overall accuracy to 82%. 14. SUBJECT TERMS 15. NUMBER OF Remote sensing, space systems operations, LiDAR, satellite laser altimetry, Optech Titan, PAGES multi-wavelength LiDAR, spectral LiDAR, terrain and building classification. 181 16. PRICE CODE 17. SECURITY 18. SECURITY 19. SECURITY 20. LIMITATION CLASSIFICATION OF CLASSIFICATION OF THIS CLASSIFICATION OF ABSTRACT REPORT PAGE OF ABSTRACT Unclassified Unclassified Unclassified UU NSN 7540–01-280-5500 Standard Form 298 (Rev. 2–89) Prescribed by ANSI Std. 239–18 i THIS PAGE INTENTIONALLY LEFT BLANK ii Approved for public release. Distribution is unlimited. Spectral LiDAR Analysis and Terrain Classification in a Semi-Urban Environment Charles A. McIver Lieutenant, United States Navy B.S., University of North Carolina at Greensboro, 2007 Submitted in partial fulfillment of the requirements for the degrees of MASTER OF SCIENCE IN SPACE SYSTEMS OPERATIONS and MASTER OF SCIENCE IN REMOTE SENSING INTELLIGENCE from the NAVAL POSTGRADUATE SCHOOL March 2017 Approved by: Richard Olsen Thesis Advisor Marcus Stefanou Co-Advisor James Newman Chair, Space Systems Academic Group Dan Boger Chair, Department of Information Sciences iii THIS PAGE INTENTIONALLY LEFT BLANK iv ABSTRACT Remote-sensing analysis is conducted for the Naval Postgraduate School campus, containing buildings, impervious surfaces (asphalt and concrete), natural ground, and vegetation. Data are from the Optech Titan scanner, providing three-wavelength laser data (532, 1064, and 1550 nm) at 10-15 points/m2. 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. ENVI’s N-Dimensional Visualizer is employed to develop training sets for supervised classification techniques, primarily Maximum Likelihood. Unsupervised classification for the combined spatial/spectral data is accomplished using a K-means classifier for comparison. The campus is classified into 10-16 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% overall classification accuracy, with grass (99.9%), turf (95%), asphalt shingles (94%), light-building concrete (89%), sand (88%), shrubs (85%), asphalt (84%), trees (80%), and clay-tile shingles (77%). Post-process filtering by ‘number of returns’ increases overall accuracy to 82%. v THIS PAGE INTENTIONALLY LEFT BLANK vi TABLE OF CONTENTS I. INTRODUCTION..................................................................................................1 A. PURPOSE OF RESEARCH .....................................................................1 B. OBJECTIVE ..............................................................................................2 II. HISTORICAL AND LITERARY REVIEW OF SPACEBORNE LIDAR SYSTEMS .................................................................................................3 A. BACKGROUND ........................................................................................3 B. U.S./NASA PLATFORMS ........................................................................5 1. Beacon Explorer—B & C and the Goddard Laser .....................5 2. Apollo 15, 16, 17 Laser Altimeters ...............................................6 3. Clementine ....................................................................................10 4. Space Shuttle LiDAR In-space Technology Experiment and Laser Altimeter Experiments ..............................................12 5. Mars Global Surveyor/Mars Orbiter Laser Altimeter.............17 6. Near Earth Asteroid Rendezvous Spacecraft—Asteroid 433 Eros.........................................................................................20 7. Phoenix Mars Lander ..................................................................23 8. Space Shuttle Triangulation + LiDAR Automated Rendezvous and Docking ............................................................25 9. Ice, Cloud, and Land Elevation Satellite/Geoscience Laser Altimeter System ...............................................................28 10. Mercury Surface, Space Environment, Geochemistry, and Ranging Orbiter....................................................................35 11. Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observations .................................................................................37 12. Lunar Reconnaissance Orbiter/Lunar Orbiter Laser Altimeter .......................................................................................40 C. OTHER PLATFORMS ...........................................................................44 1. l’Atmosphere Par LiDAR Sur Saliout (“Space Station Atmospheric LiDAR”)—French/Russian LiDAR on the Mir Space Station .........................................................................44 2. Hayabusa Asteroid Probe—Japan .............................................45 D. THE WAY AHEAD FOR LIDAR IN SPACE ......................................46 1. NASA—Next Generation Spacecraft Landing Integrated LiDAR ...........................................................................................46 2. NASA—Advanced Topographic Laser Altimeter System and Swath Imaging Multi-polarization Photon-counting LiDAR ...........................................................................................49 vii 3. NASA—Global Ecosystem Dynamics Investigation LiDAR ...........................................................................................53 4. Sigma Space—Single Photon-Counting 3D LiDAR..................55 5. Multi-wavelength LiDAR for Terrain Classification ...............57 III. DATASET AND PREPARATIONS ..................................................................59
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