Terrain Generation:
LIDAR and Hyperspectral
      Data Fusion
 and Feature Extraction




                                ...
Urban Terrain Modeling and Simulation




              Training and Visualization




    Modeling and Simulation




   ...
Urban Terrain Modeling and Simulation




PREXXXX 3
PREXXXX 4
Acquisition Sensor Configuration

   LEICA         ALS-50+ LIDAR
           0.5-meter point spacing
   AISA        Eagl...
Acquired LIDAR Flightlines




PREXXXX 6
Mosaicked and Tiled LIDAR Flightlines

              Site A               Site B




PREXXXX 7
Raw LIDAR Reflectance (Elevation)




PREXXXX 8
Hyperspectral Fusion to LIDAR Reflectance




PREXXXX 9
MARS Software Classified LIDAR points




PREXXXX 10
MARS 3D Viewer: Classified LIDAR Points




PREXXXX 11
MARS Profile: Classified LIDAR Points (Urban)




PREXXXX 12
MARS Profile: Classified LIDAR Points (Vegetated Urban)




PREXXXX 13
MARS Profile: Classified LIDAR Points (Urban Residential)




PREXXXX 14
Classified LIDAR points: Urban-Residential




PREXXXX 15
Hyperspectral Scanners




PREXXXX 16
Acquired Flightlines Hyperspectral

                                Site A Flightlines and Mosaic




PREXXXX 17
Acquired Flightlines Hyperspectral

                               Site B Flightlines and Mosaic




PREXXXX 18
Atmospheric Correction: Radiative Transfer Model


                        Rsp(x,y,w) = [ Rdn(x,y,w) * Gain(W) ] + Offset(...
Atmospheric Correction Model Input Parameters
        Input Image file: filename: 1013-1043_rad.dat
        Image Dimens...
Atmospheric Correction: Concrete-Asphalt Spectral Curves




PREXXXX 21
Atmospheric Correction: Vegetation - Grass




PREXXXX 22
Hyperspectral Digital Orthoimagery

         Hyperspectral Bands: (82, 36, 12)   Hyperspectral Bands: (63, 36, 12)




PRE...
Sensor Fusion Feature Extraction
                                                              Urban Terrain Database
    ...
Hyperspectral Land Cover Classification




PREXXXX 25
Hyperspectral Classification: Paved Surfaces

                               Paved Surfaces: Road, Lots, Walkways
        ...
Hyperspectral Classification: Buildings

                               Building Rooftop Material Composition
            ...
Hyperspectral Classification: Paved Surfaces

                               Tree Canopies, Brush, Grass, Wetland Veg
    ...
Hyperspectral Classification




PREXXXX 29
Building Material Composition




PREXXXX 30
Hyperspectral Classified Water Bodies + DTM




PREXXXX 31
Fusion of LIDAR and Hyperspectral Classification




PREXXXX 32
Hyperspectral Classification: ESRI Shape Files




PREXXXX 33
Fusion 3D Perspective: ESRI Shape Files




PREXXXX 34
Building Model Generator: 3D Footprint to Synthetic Model




                                                 2

        ...
Urban Terrain Model to 3D Training Visualization




PREXXXX 36
Conclusion

            The fusion of LIDAR and Hyperspectral data provides a means by
             which to efficiently ...
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Lidar hsi datafusion ilmf 2010

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Lidar hsi datafusion ilmf 2010

  1. 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. 2. Urban Terrain Modeling and Simulation Training and Visualization Modeling and Simulation Data Acquisition and Feature Extraction PREXXXX 2
  3. 3. Urban Terrain Modeling and Simulation PREXXXX 3
  4. 4. PREXXXX 4
  5. 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) PREXXXX 5
  6. 6. Acquired LIDAR Flightlines PREXXXX 6
  7. 7. Mosaicked and Tiled LIDAR Flightlines Site A Site B PREXXXX 7
  8. 8. Raw LIDAR Reflectance (Elevation) PREXXXX 8
  9. 9. Hyperspectral Fusion to LIDAR Reflectance PREXXXX 9
  10. 10. MARS Software Classified LIDAR points PREXXXX 10
  11. 11. MARS 3D Viewer: Classified LIDAR Points PREXXXX 11
  12. 12. MARS Profile: Classified LIDAR Points (Urban) PREXXXX 12
  13. 13. MARS Profile: Classified LIDAR Points (Vegetated Urban) PREXXXX 13
  14. 14. MARS Profile: Classified LIDAR Points (Urban Residential) PREXXXX 14
  15. 15. Classified LIDAR points: Urban-Residential PREXXXX 15
  16. 16. Hyperspectral Scanners PREXXXX 16
  17. 17. Acquired Flightlines Hyperspectral Site A Flightlines and Mosaic PREXXXX 17
  18. 18. Acquired Flightlines Hyperspectral Site B Flightlines and Mosaic PREXXXX 18
  19. 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 PREXXXX 19
  20. 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 PREXXXX 20
  21. 21. Atmospheric Correction: Concrete-Asphalt Spectral Curves PREXXXX 21
  22. 22. Atmospheric Correction: Vegetation - Grass PREXXXX 22
  23. 23. Hyperspectral Digital Orthoimagery Hyperspectral Bands: (82, 36, 12) Hyperspectral Bands: (63, 36, 12) PREXXXX 23
  24. 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 PREXXXX 24
  25. 25. Hyperspectral Land Cover Classification PREXXXX 25
  26. 26. Hyperspectral Classification: Paved Surfaces Paved Surfaces: Road, Lots, Walkways Spectral Curves PREXXXX 26
  27. 27. Hyperspectral Classification: Buildings Building Rooftop Material Composition Spectral Curves PREXXXX 27
  28. 28. Hyperspectral Classification: Paved Surfaces Tree Canopies, Brush, Grass, Wetland Veg Spectral Curves PREXXXX 28
  29. 29. Hyperspectral Classification PREXXXX 29
  30. 30. Building Material Composition PREXXXX 30
  31. 31. Hyperspectral Classified Water Bodies + DTM PREXXXX 31
  32. 32. Fusion of LIDAR and Hyperspectral Classification PREXXXX 32
  33. 33. Hyperspectral Classification: ESRI Shape Files PREXXXX 33
  34. 34. Fusion 3D Perspective: ESRI Shape Files PREXXXX 34
  35. 35. Building Model Generator: 3D Footprint to Synthetic Model 2 U2MG models physical properties of structures. PREXXXX 35
  36. 36. Urban Terrain Model to 3D Training Visualization PREXXXX 36
  37. 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 PREXXXX 37

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