Contenu connexe Similaire à Measuring Change with Radar Imagery_Richard Goodman - Intergraph Geospatial World Tour 2013 (20) Measuring Change with Radar Imagery_Richard Goodman - Intergraph Geospatial World Tour 20132. Why is change measurement important
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3. Why is change measurement important?
Devastation caused by tornadoes in Alabama, 2011
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4. Why is change measurement important?
Magnitude 9.0 earthquake in Japan, 2011
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5. Why is change measurement important?
Mississippi river flooding in Memphis, Tennessee, 2011
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7. Why is change measurement important?
Oil spills
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8. So why use RADAR data in change
measurement
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10. Why use RADAR data in change measurement?
• Active sensor
• No sun illumination required
• Imagery collected in cloudy weather
• Pulse sent from sensor, reflected off features, return captured by
antenna
• SINGLE LOOK COMPLEX (SLC) data format
• RADARSAT 1 / 2, COSMO-SkyMed, TerraSAR-X
• 2 bands of data
• Magnitude (strength of return)
• Phase (position of wave on return to antenna)
• Man-made features and infrastructure give
strong returns
• Buildings
• Steel/metals
• Sub-surface features
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11. Why use RADAR data in change measurement?
• Interferometric processing
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Interferometric pairs
Make use of both magnitude and phase layers
Coherence Change Detection (CCD)
Differential Interferometric Displacement Mapping (D-InSAR)
• Accuracy to cm level
• Monitoring subsidence
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Subsurface oil and water extraction
Mining
Natural disasters – earthquakes, volcanoes
Human activities
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14. ERDAS Imagine Radar Tools
• Radar Mapping Suite
• Add-on module
• Operational software
• Not a toolkit!
• Directly read data into viewer
• No import required
• No resampling of data
• New Radar Analyst ribbon
• Fast feature extraction
• Visualisation aids
• Interferometry tools
• CCD
• D-InSAR
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15. ERDAS Imagine Radar Tools
• New Radar Analyst ribbon
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16. ERDAS Imagine Radar Tools
1. Project Setup
2. Processing Steps
• Interferometry tools – CCD & D-InSAR
• Wizard-based workflows
• Intelligent defaults for parameters
• Experts can take full control
3. Visual
Interpretation
• Phase Un-Wrap algorithm
• The heart of the CCD and D-InSAR
• Developed in conjunction with German Aerospace Agency (DLR)
4. Automatic Detection
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19. KSAT DACT Case Study
• Who/What is KSAT
• Kongsberg Satellite Services AS (KSAT) is a commercial
Norwegian enterprise
• Provides services to European Maritime Safety Agency to detect
and report oil spills using RADAR satellite imagery
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21. KSAT DACT Case Study
• Locations
Svalbard Satellite Station (SvalSat)
at 78°15´N 15°80´E
19 employees
Tromsø Station & Offices at
69°39´N 18°56´E.
80 Employees
Grimstad, (South Norway) at
58°20´N 8°21´E
4 employees
And TrollSat (Antarctica) at 72°S 2°E – That s far far south…
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22. KSAT DACT Case Study
• The DACT Project
• KSAT has provided a service using satellite images for detection of
oil spills since 1994
• Can deliver oil spill reports to European end users promptly
• New user requirements had KSAT develop the Data Analysis and
Classification Tool with emphasis on oil spill and vessel detection
from satellite imagery
• Radar imagery ideal for quickly identifying oil spills and linking them
to possible sources (vessels/platforms)
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23. KSAT DACT Case Study
• The System
• Customised Imagine ribbon interface with KSAT tab
• Contains shortcuts to common tools used in identification, extraction
of oil spills
• External data sources, shipping tracks, wind, wave, current, oil
platforms used to identify possible sources of spill
• Reports generated for each radar scene (clean sea, warning,
notification)
• Target turnaround time is 30 minutes from when imagery available to
report sent
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27. Qualitative Change Mapping with CCD
Magnitude Change
Average Radar Magnitude
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Coherence Change
Phase Coherence
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29. Quantitative Change Mapping with D-InSAR
Using the IMAGINE D-InSAR tool
users can map Vertical
Displacement
Max Subsidence = -16.5 cm
Max Uplift = +26.6 cm
Published Displacement: +30/-18
Xia,Ye. Bam earthquake: Surface deformation
measurement using radar interferometry. Acta
Seismologicia Sinica, Vol.18 No.4 (451~459), July,
2005
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30. Summary – Operational Change Measurement Workflow
Input raw RADAR data
Vector output
Detected change
Report
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