http://lidarnews.com/breaklines-and-lidar-101

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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