1. R I T Rochester Institute of Technology HYCODE Meeting MURI Overview January 16, 2003 Rochester Institute of Technology Cornell University University of California, Irvine WHO WE ARE PHILOSPHY OF APPROACH CAPABILITIES DATA NEEDS

2. R I T Rochester Institute of Technology Multi-University Research Initiative MURI Team RIT Team (PI: Dr. John Schott) – Modeling and Simulation » Scott Brown – Water Quality and Biological Activity and Material Classification and ID » Rolando Raqueno, Jason Hamel, Adam Goodenough – Atmospheric Parameter Retrieval/Correction » Emmett Ientilucci, Marvin Boonmee – Gaseous Effluent Detection and Quantification » Dr. David Messinger, Tim Hattenberger, Erin O’Donnell

3. R I T Rochester Institute of Technology Multi-University Research Initiative MURI Team UCI Team (PI: Dr. Glenn Healey) – Grad Student Team Cornell Team (PI: Dr. William Philpot) – Dr. Minsu Kim

4. R I T Rochester Institute of Technology MURI Overview Scope of Project (what we said we’d do) – build a new generation of hyperspectral data processing algorithms » in a physics-based modeling environment » with strong connectivity to the phenomenology – Focus on Four Application Areas: » Water Quality and Biological Activity » Material Classification and ID » Atmospheric Modeling & Compensation » Gaseous Effluent Detection & Quantification

5. R I T Rochester Institute of Technology Physics Based Model Adjust Model Inputs to Optimize Match Input Parameters Observed Values Model Outputs Model Matching Concept

6. R I T Rochester Institute of Technology Water Quality and Biological Activity (Littoral Modeling) Summary Preliminary concentration maps for Lake Ontario and LEO-15 PHILLS scenes Exercising Tafkaa algorithm (NRL atmospheric compensation) Initial Photon-mapping tools for complex littoral scene modeling Investigation of littoral test scene and plan for tool validation

7. R I T Rochester Institute of Technology Target Detection Efforts Summary Invariant subspace algorithm code validated Improvements to basis vector selection process Threshold selection scheme defined Tests for different target spectra and different scenes

8. R I T Rochester Institute of Technology AVIRIS Image Test Scenario

9. R I T Rochester Institute of Technology Compute All Possible Sensor Reaching Radiances Target TargetReflectance All possible Modeled Atmospheres Target Subspace

10. R I T Rochester Institute of Technology Original AVIRISResult AVIRISPixel Target Detection for the Basketball Court Spectrum AVIRISPixel Basketball Court Spectrum

11. R I T Rochester Institute of Technology Candidate Area North Rochester, NY Good spatial coverage & data availability Good spatial resolution – IKONOS » 4 meter RGB+NIR channels » 1 meter Panchromatic » 30 meter coregistered DEM » (currently using 10 m USGS) Candidate site – North-east corner of city – About 2.8 x 2.3 miles North Ikonos

12. R I T Rochester Institute of Technology Scene Overview DakeSchoolTile 1 2 3 4 5 6 Ikonos

13. R I T Rochester Institute of Technology DIRSIG Megascene Ikonos ImageSynthetic Image

14. R I T Rochester Institute of Technology Hyperspectral Synthetic Imagery Color image made from a simulated HYDICE flight line DIRSIG Megascene

15. R I T Rochester Institute of Technology DIRS capabilities for field sampling and in-water Measurements (Dr. Tony Vodacek)

16. R I T Rochester Institute of Technology Water optical modeling – Cornell Team Ocean Optical Phytoplankton Model (OOPS) Existing ocean color algorithms oversimplify phytoplanktonExisting ocean color algorithms oversimplify phytoplankton (plankton IOP’s are a simple function of chlorophyll conc.) OOPS includesOOPS includes realistic pigment components – realistic pigment components – size distribution – particle shape – packaging effect OOPS provides bulk optical propertiesOOPS provides bulk optical properties – scattering phase function – absorption

17. R I T Rochester Institute of Technology HYDROLIGHT Derived Maps for LEO-15 and Lake Ontario

18. R I T Rochester Institute of Technology Basic Hydrolight World CHLTSSCDOM Bottom Reflectance Solar and AtmosphericRadiance Cloud Air/Water interface SecchiDiskTarget

19. R I T Rochester Institute of Technology More realistic approach CHLTSSCDOM Cloud Solar and AtmosphericRadiance Air/Water interface Ray Tracing within the water Bottom Reflectance and Angle SurfaceObjects UnderwaterObjects

20. R I T Rochester Institute of Technology Jensen’s Photon Mapping Examples Optimization of Monte Carlo Technique from Computer Graphics Community

21. R I T Rochester Institute of Technology DATA REQUEST FROM THE HYCODE COMMUNITY Guidance in suggesting other hyperspectral airborne scenes Looking for Ground Truth data associated with field collects – (LEO-15) July 31, 2001 Measured IOPs for input into models (HYDROLIGHT, etc.) – CDOM, Chlorophyll, TSS – Phase functions (or Fournier-Forand estimates) Bathymetry and Benthic Maps Comments and Suggestions rolando@cis.rit.edu

22. R I T Rochester Institute of Technology Candidate Journals and Conferences for Publications Optical Engineering (SPIE) IGARSS (IEEE) Remote Sensing of Environment

23. R I T Rochester Institute of Technology

24. R I T Rochester Institute of Technology Temperature Prediction: Radiational Exchange RIT’s Bendix LWIR line-scanner DIRSIGSimulation Warmer regions between houses are reproduced due to decreased sky exposure and increased exchange with warmer surfaces. Winter nights in Rochester, NY