Adjoint Radiosity Based Algorithms For Retrieving Target Reflectances In Urban Area Shadows  Dr. Christoph Borel , Kenneth...
Content <ul><li>Problem of determining reflectance for high-spatial resolution imagers </li></ul><ul><li>Why is the illumi...
RS radiance model – is this complete? <ul><li>6S radiance *  model has  illumination  and  atmospheric terms: </li></ul><u...
Illumination in an urban environment <ul><li>High-spatial imagers (QuickBird, Ikonos, airborne) resolve shadows of buildin...
Contribution from nearby surfaces in shade <ul><li>Compute the fractions of  the sensor radiance=( path radiance) +( Sky r...
Adjoint Radiosity is uniquely suited for remote sensing ( Inverse Global Illumination   Paul Debevec, Siggraph 99 ) Comput...
New method needed to perform reflectance retrieval Note:  Because of complexity of problem our current focus is limited to...
2-D corner radiosity model with direct solar and sky light 1 2 θ sun F 12 >>F 21 S=tan( θ sun ) B 2a  B 2b B 1 f 1
Rendering of a “street canyon” <ul><li>Notes: </li></ul><ul><li>The intensity in the shadow varies with the size of the op...
Fast skyview factor using shadow volume Shadow volume (SV) of DEM DEM of London Shadow=(DEM eq SV) Add all shadow images R...
A walk in downtown Dayton using LIDAR data
Experiment to  retrieve illumination
Conclusions and potential applications  <ul><li>Conclusions: </li></ul><ul><li>The contribution of illumination from nearb...
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Adjoint Radiosity Borel Earsel09 2 11 09 White

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This presentation got the "best oral presentation award" at EARSEL in Tel Aviv.

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Adjoint Radiosity Borel Earsel09 2 11 09 White

  1. 1. Adjoint Radiosity Based Algorithms For Retrieving Target Reflectances In Urban Area Shadows Dr. Christoph Borel , Kenneth Ewald, Mark Manzardo, Dr. Charles Wamsley and John Jacobson* Ball Aerospace and *NASIC [email_address] 6 th EARSEL SIG IS workshop2009 Tel Aviv
  2. 2. Content <ul><li>Problem of determining reflectance for high-spatial resolution imagers </li></ul><ul><li>Why is the illumination determination important? </li></ul><ul><li>How does adjoint radiosity address the reflectance retrieval problem? </li></ul><ul><li>A simple radiosity model of a wall </li></ul><ul><li>How LIDAR data can be used to determine illumination from sky </li></ul><ul><li>Experiment to retrieve illumination </li></ul><ul><li>Conclusions </li></ul>
  3. 3. RS radiance model – is this complete? <ul><li>6S radiance * model has illumination and atmospheric terms: </li></ul><ul><li>Where: </li></ul><ul><li>L m = measured radiance, </li></ul><ul><li> = surface reflectance, </li></ul><ul><li>s =spherical albedo of atmosphere </li></ul><ul><li><  >= adjacency filtered reflectance, </li></ul><ul><li>E 0 = solar irradiance, </li></ul><ul><li> s = transmission from sun to surface,  =  direct +  diff, where  direct = direct and  diff , = diffuse transmission from ground to sensor, and </li></ul><ul><li>L p = path radiance. </li></ul><ul><li>* http://6s.ltdri.org/ </li></ul>
  4. 4. Illumination in an urban environment <ul><li>High-spatial imagers (QuickBird, Ikonos, airborne) resolve shadows of buildings. </li></ul><ul><li>The simple 6S model assumes direct solar illumination and can’t be used in shadow! </li></ul><ul><li>Analysis of the contribution from near-by surfaces illuminating a surface: </li></ul><ul><ul><li>A numerical experiment will show the importance of illumination from a near-by surface for a point in shade </li></ul></ul><ul><ul><li>Simple 2-d radiosity model for a corner representing a façade shadow. </li></ul></ul><ul><ul><li>Compute sky fraction in urban environment from LIDAR data. </li></ul></ul>1 1 2 2
  5. 5. Contribution from nearby surfaces in shade <ul><li>Compute the fractions of the sensor radiance=( path radiance) +( Sky reflected down-welling sky radiance )+( reflected downwelling radiance from surrounding objects ) +( atmospheric adjacency radiance ) of a shaded pixel next to a vertical wall in shadow. </li></ul><ul><li>All surfaces have 50% reflectance, MLS profile, nadir viewing geometry, 23km visibility with rural aerosols, 30º Sun zenith, space to ground look. </li></ul>Simulation: 21% 47% 63% Result: The light scattered from nearby objects contributes 15-80%! Fractional contribution to total radiance
  6. 6. Adjoint Radiosity is uniquely suited for remote sensing ( Inverse Global Illumination Paul Debevec, Siggraph 99 ) Computer graphics rendering: Realistic scene generation through global illumination using known reflectance, geometry and illumination Adjoint radiosity method: Retrieve reflectances from known radiance image, geometry and estimated illumination -> This is the best solution to reflectance retrieval problem! Illumination Reflectance Radiance Image Geometry Radiance Image Illumination Reflectance Geometry Remote sensing problem Computer graphics rendering problem unknown known Forward problem Inverse problem
  7. 7. New method needed to perform reflectance retrieval Note: Because of complexity of problem our current focus is limited to illumination correction Atmospheric Correction Calibration HSI Sensor DN L m (x,y, λ ) L sky ( θ , φ , λ ) Current focus Illumination Correction ρ (x,y, λ ) 3-D model Sun L surf (x,y, λ ) Atmosphere
  8. 8. 2-D corner radiosity model with direct solar and sky light 1 2 θ sun F 12 >>F 21 S=tan( θ sun ) B 2a B 2b B 1 f 1
  9. 9. Rendering of a “street canyon” <ul><li>Notes: </li></ul><ul><li>The intensity in the shadow varies with the size of the opening. </li></ul><ul><li>The shadow of the right wall is always darker than the shadows between the two walls. </li></ul><ul><li>The partially shaded horizontal surface is always darker than the fully shaded vertical surfaces. </li></ul>
  10. 10. Fast skyview factor using shadow volume Shadow volume (SV) of DEM DEM of London Shadow=(DEM eq SV) Add all shadow images Reference: Ratti C, Richens P.,1997 http://www.bath.ac.uk/pip/directory/showperson?personId=502162 Principle: A surface is in shade if the height of the shadow is higher than the surface.
  11. 11. A walk in downtown Dayton using LIDAR data
  12. 12. Experiment to retrieve illumination
  13. 13. Conclusions and potential applications <ul><li>Conclusions: </li></ul><ul><li>The contribution of illumination from nearby surfaces can dominate the illumination in shadows, e.g. more than 50% in SWIR. </li></ul><ul><li>Retrieve reflectance of objects in complex situations, e.g. in building and canopy shade, under partly cloudy conditions, under cloudy conditions. </li></ul><ul><li>Potential applications: </li></ul><ul><li>Enhance imagery in shaded regions by suppressing the direct solar term. </li></ul><ul><li>Novel atmospheric retrieval techniques to retrieve aerosol parameters from shadow intensity. </li></ul><ul><li>Realistically embed computer generated targets using correct illumination field. </li></ul><ul><li>Relight 3-D scenes to simulate different illumination conditions. </li></ul><ul><li>Change detection for imagery taken under different illumination conditions, e.g. illumination can be changed after retrieval. </li></ul>

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