This document discusses the fusion of LIDAR and hyperspectral data to generate 3D urban terrain databases for modeling and simulation. It describes acquiring LIDAR and hyperspectral data over two sites, data processing including atmospheric correction and classification, and fusing the data to extract features and generate a database with 3D shapes, land cover maps, and building models. The conclusion states that combining LIDAR and hyperspectral data provides an efficient way to create detailed 3D urban databases for realistic training simulations.

1. Terrain Generation:
LIDAR and Hyperspectral
Data Fusion
and Feature Extraction
Authors:
Raul Campos-Marquetti and Robert Sours
Engineering | Architecture | Design-Build | Surveying | GeoSpatial Solutions

2. Urban Terrain Modeling and Simulation
Training and Visualization
Modeling and Simulation
Data Acquisition and Feature Extraction
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3. Urban Terrain Modeling and Simulation
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5. Acquisition Sensor Configuration
 LEICA ALS-50+ LIDAR
 0.5-meter point spacing
 AISA Eagle Hyperspectral
 0.6-meter pixel resolution
 128 spectral bands (397.8-nm to 997.96-nm)
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6. Acquired LIDAR Flightlines
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7. Mosaicked and Tiled LIDAR Flightlines
Site A Site B
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8. Raw LIDAR Reflectance (Elevation)
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9. Hyperspectral Fusion to LIDAR Reflectance
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10. MARS Software Classified LIDAR points
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11. MARS 3D Viewer: Classified LIDAR Points
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12. MARS Profile: Classified LIDAR Points (Urban)
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13. MARS Profile: Classified LIDAR Points (Vegetated Urban)
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14. MARS Profile: Classified LIDAR Points (Urban Residential)
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15. Classified LIDAR points: Urban-Residential
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16. Hyperspectral Scanners
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17. Acquired Flightlines Hyperspectral
Site A Flightlines and Mosaic
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18. Acquired Flightlines Hyperspectral
Site B Flightlines and Mosaic
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19. Atmospheric Correction: Radiative Transfer Model
Rsp(x,y,w) = [ Rdn(x,y,w) * Gain(W) ] + Offset(w)
Rsurf(x,y,w) = [ R0(x,y,w) / {Rsol(w) x T(w) x cos(theta)} ] – Rpath(w)
where:
Rsp: Spectral Radiance at sensor
Rdn: Grey Scale Value
Gain: Measure of max radiance instrument response
Offset: Dark Current Radiance (measure of internal system background noise)
Rsurf: Surface Reflectance
Ro: Observed Radiance at Sensor
Rsol: Solar Irradiance above the earth’s atmosphere
T: total Atmospheric Transmittance
Rpath: Path Radiance
(theta): Incidence Angle
(x,y): Pixel x,y, cooridnates
W: Wavelength
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20. Atmospheric Correction Model Input Parameters
 Input Image file: filename: 1013-1043_rad.dat
 Image Dimension (bands,samples,lines,offset): (128, 965, 7208, 0)
 Latitude / Longitude (deg, min, sec): ( 28.5874 0.0 0.0 N / -81.2243 0.0 0.0 W)
 Image date (day, month, year): 13 10 2009
 Image average time (UTC) (hour, minute, second): 14 45 19
 Image mean elevation (meters): -11
 Image acquisition altitude (kilometers): 0.8855
 Atmospheric Model (1=ml summer, 2=ml winter, 3=tropic): 1
 Derive water vapor (0=no, 1=940, 2=1140, 3=both, 4=820): 1
 Path Radiance in water vapor fit (0=no, 1=yes): 1
 Image Atmosphere Visibility(5 to 250 kilometers): 30
 Estimated Visibility (1=yes, 0=no): 1
 Image Spectral Calibration file: Spectral_Calibration_OSPREY.txt
 Artifact supression type 1,2,3 (1=yes, 0=no):1 1 1
 Gain file: Image_Gain_OSPREY.txt (0.01)
 Offset file: Image_Offset_OSPREY.txt (0.0)
 Output reflectance image file: 1013-1043_ref.dat
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21. Atmospheric Correction: Concrete-Asphalt Spectral Curves
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22. Atmospheric Correction: Vegetation - Grass
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23. Hyperspectral Digital Orthoimagery
Hyperspectral Bands: (82, 36, 12) Hyperspectral Bands: (63, 36, 12)
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24. Sensor Fusion Feature Extraction
Urban Terrain Database
LAS
Bare Earth Surface
Tree Canopy Points
Buildings – Utilities
GeoTiff
DTM – Elevation
Shaded Relief
RGB / CIR Ortho
Shape Files
Buildings
Street Centerline
Edge of Pavement
Lots
Powerlines
Soils
Acquisition Data Fusion Vegetation
LIDAR LIDAR
LIDAR Extraction Tree Canopy
Hyperspectral Hyperspectral
MARS software Grass
AGPS/IMU and Classification
ENVI - ArcMap Bare Earth Surface
Brush
Vegetation Canopy Wetland Veg
Buildings - Utilities Lakes-Ponds-River
Material Composition
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25. Hyperspectral Land Cover Classification
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26. Hyperspectral Classification: Paved Surfaces
Paved Surfaces: Road, Lots, Walkways
Spectral Curves
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27. Hyperspectral Classification: Buildings
Building Rooftop Material Composition
Spectral Curves
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28. Hyperspectral Classification: Paved Surfaces
Tree Canopies, Brush, Grass, Wetland Veg
Spectral Curves
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29. Hyperspectral Classification
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30. Building Material Composition
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31. Hyperspectral Classified Water Bodies + DTM
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32. Fusion of LIDAR and Hyperspectral Classification
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33. Hyperspectral Classification: ESRI Shape Files
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34. Fusion 3D Perspective: ESRI Shape Files
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35. Building Model Generator: 3D Footprint to Synthetic Model
2
U2MG models physical
properties of structures.
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36. Urban Terrain Model to 3D Training Visualization
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37. Conclusion
 The fusion of LIDAR and Hyperspectral data provides a means by
which to efficiently generate the base data for 3D Urban Databases
 Provides Real World Locations and Feature Classes
 Real World Coordinates for features (x,y,z)
 Land Cover and Land Use Classes
 Physical morphology of features and Attribute extraction
 Material Composition of feature infrastructure
 Generation of 3D feature Objects for use in Modeling & Simulation
environments, providing realistic training scenarios
Raul Campos-Marquetti / Remote Sensing Solutions Manager
Email: rcmarquetti@merrick.com
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