Transcript The Laboratory for Imaging Algorithms and Systems

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
RIT
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
RIT
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
RIT
Rochester Institute of Technology
MURI Overview
Lake Ontario
•
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
RIT
Rochester Institute of Technology
Model Matching Concept
Input
Parameters
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Physics
Based
Model
Adjust Model
Inputs to Optimize
Match
RIT
Rochester Institute of Technology
Model
Outputs
Observed
Values
 oˆ1 
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Water Quality and Biological Activity
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
RIT
Rochester Institute of Technology
(Littoral
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
RIT
Rochester Institute of Technology
AVIRIS Image Test Scenario
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Reflectance
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0
50
100
Band
RIT
Rochester Institute of Technology
150
200
Compute All Possible
Sensor Reaching Radiances
0.5
Reflectance
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0
50
100
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Band
Target
Reflectance
RIT
Rochester Institute of Technology
All possible
Modeled
Atmospheres
Target Subspace
Target Detection for the Basketball Court Spectrum
Original AVIRIS
Result
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Reflectance
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AVIRIS
Pixel
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50
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150
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Band
Basketball Court Spectrum
RIT
Rochester Institute of Technology
AVIRIS
Pixel
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
North
USGS)
• Candidate site
– North-east corner of city
– About 2.8 x 2.3 miles
RIT
Rochester Institute of Technology
Ikonos
Scene Overview
6
5
4
3
2
Dake
School
Tile
1
Ikonos
RIT
Rochester Institute of Technology
DIRSIG Megascene
Ikonos Image
RIT
Rochester Institute of Technology
Synthetic Image
Hyperspectral Synthetic Imagery
DIRSIG Megascene
Color image made from a simulated HYDICE flight line
RIT
Rochester Institute of Technology
DIRS capabilities
for field sampling
and in-water
Measurements
(Dr. Tony Vodacek)
RIT
Rochester Institute of Technology
Water optical modeling – Cornell Team
Ocean Optical Phytoplankton
Model (OOPS)
• Existing ocean color algorithms
oversimplify phytoplankton
(plankton IOP’s are a simple
function of chlorophyll conc.)
• OOPS includes
– realistic pigment components
– size distribution
– particle shape
– packaging effect
• OOPS provides bulk optical
properties
– scattering phase function
– absorption
RIT
Rochester Institute of Technology
HYDROLIGHT Derived Maps
for LEO-15 and Lake Ontario
Products
R
R.II.T
T
RIT
Digital Imaging and Remote Sensing Laboratory
Rochester Institute of Technology
Basic Hydrolight World
Cloud
Solar and
Atmospheric
Radiance
CHL
TSS
CDOM
Air/Water interface
Bottom Reflectance
RIT
Rochester Institute of Technology
Secchi
Disk
Target
More realistic approach
Solar and
Atmospheric
Radiance
Cloud
Ray Tracing
within the
water
Surface
Objects
Air/Water interface
CHL
TSS
CDOM
Underwater
Objects
Bottom Reflectance
and Angle
RIT
Rochester Institute of Technology
Jensen’s Photon Mapping Examples
Optimization of Monte Carlo Technique from
Computer Graphics Community
RIT
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
[email protected]
RIT
Rochester Institute of Technology
Candidate Journals and Conferences for
Publications
• Optical Engineering (SPIE)
• IGARSS (IEEE)
• Remote Sensing of Environment
RIT
Rochester Institute of Technology
RIT
Rochester Institute of Technology
Temperature Prediction:
Radiational Exchange
RIT’s Bendix
LWIR line-scanner
Winter nights in
Rochester, NY
DIRSIG
Simulation
Warmer regions between houses are reproduced due to decreased
sky exposure and increased exchange with warmer surfaces.
RIT
Rochester Institute of Technology