Global Geospatial Conference 2012 - Global Spatial Data ...

6/23/12
Québec, 14-17 May 2012
Global Geospatial Conference 2012
High resolution spaceborne imagery for emergency response
through faster image processing and analysis using cuttingedge
remote sensing algorithms
Dubois, David
Lepage, Richard
Benoit, Mathieu
• Background
• Very high resolution images
• Methodology
• Applications
• Conclusions
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• Major disasters cause many casualties and
material losses each year.
• Steps in emergency management cycles [1]:
– Mitigation
– Prevention
– Preparedness
– Detection
– Response
– Recovery
– Evaluation
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• International Charter « Space and Major
Disasters »
– Aims at providing a unique gateway to request and
obtain space acquired data when a natural or manmade
disaster strikes
– Prepared maps and analysis can be used for
response, recovery and evaluation
– Requests can only be made by Authorized Users
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• Members
– ESA: European Space Agency
– CNES: France’s Centre National d’Études Spatiales
– CSA: Canadian Space Agency
– NOAA, ISRO, CONAE, USGS, JAXA, CNSA,
DLR, KARI, INPE
• Each member commits resources to support the
Charter
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• 2011 – Earthquake and tsunami hitting Japan’s
north-east coast
– Fukushima Daiichi nuclear catastrophe
– Multiple coastal villages
destroyed
– More than 200 000 people
evacuated
©USGS - Map produced by AIT - RADARSAT-2 Data and Products © MacDonald, Dettwiler and Associates
Ltd.(2011) - All Rights Reserved. RADARSAT is an official trademark of the Canadian Space Agency.
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• 2010 – Earthquake hitting Haiti’s main cities
– More than 300 000 deaths
– More than 1 million homeless
– 250 000 residences and 30 000 commercial
buildings destroyed
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• Time is critical. Haste is vital!
Disaster
Charter
activation
Satellite
tasking
Image
acquisition
Response
Image
download
Crisis
information
Image
analysis
Image preprocessing
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• Tasking of satellites (Priority and uplink delays)
• Acquisition of raw images
• Transfer of raw images
• Availability of archive data
• Size of images
• Image analysis and interpretation
• Map creation
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• Reduce preparation time of damage maps in order
to provide adequate information for the response
phase of the emergency management cycle by
detecting buildings and evaluating damage.
• Objectives
– Automate parts of the process for building detection
– Provide object matching capacities between
unregistered archive and acquisition images
– Cut processing times for the preparation of value
added products
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• Very high spatial resolution sensors can help in
the identification of infrastructure and building
damages [2-3]
• Current and planned sensors pros:
– A variety of spatial and spectral resolutions
– Agility to change acquisition angle to shorten delays
between acquisition dates
– Short revisit time
• Cons:
– Images are HUGE (40 000 x 40 000 pixels for a scene)
– Unwanted details (cars, individual trees and shrubs)
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• Optical sensors
Satellite Sensor types Resolution (m) Revisit (days)
GeoEye-1 PAN/XS(4) 0,5/2 2 to 8
IKONOS PAN/XS(4) 1/4 3
Pléiades-1 PAN/XS(4) 0,5/2 1*
Quickbird 2 PAN/XS(4) 0,6/2,4 1 to 4
WorldView-1 PAN 0,5 2 to 6
WorldView-2 PAN/XS(8) 0,5/1,8 to 2,4 1 to 4
* When the constellation is operational
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• Synthetic Aperture Radar (SAR)*
Satellite Freq. band Resolution (m) Revisit (days)
COSMO/SkyMed X 1 0,25 to 0,5**
Radarsat-2 C 1 24
TerraSAR-X X 1 11
* Only spotlight capable satellites are presented
** Constellation
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• GeoEye-2
• Pléiades-2
• Spot-6 and Spot-7
• RADARSAT Constellation Mission (RCM)
Keyword for future missions: CONSTELLATION
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• Requirements
– Semi-automated process
– Few parameters
– Fast processing
– High accuracy
• Recent advances
– Multi-scale segmentation
– Object-based classification and analysis
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• Level lines/level sets
• Fast Level Set Transform (FLST) [4]
– Hierarchical representation ideal for urban regions
• Why?
– Getting objects from pixels
Image
...
City block
Roofs
Details
Pixel
...
... ... ...
...
...
...
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• Associate shape for each pixel
– Local scale extraction [5]
– Most representative scale for objects
• Shape-driven segmentation
– Area of shapes to consider (min, max)
– Accounting for sharpness of shape edges
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• Faster processing
– 1 object vs 100 pixels
• Relationnal analysis
– Object A is close to object B
• Geometrical features
– Invariance to rotation, translation and possibly
scale
– Hierarchical shape relations is discriminant
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• Building detection
– Rescue operations
– Identification of types (residential, commercial,
industrial)
• Damage evaluation
– Planning
– Reconstruction
• What is needed?
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• Segmentation (FLST+Scale) and classification
(SVM) of shapes
Object-based
Pixel-based
– Building
Producer Accuracy (%) 93.6 94
– Not building User Accuracy (%) 93.3 88
False Positive Rate (%) 4.5 7.4
• Good results with high User Accuracy and low
Execution time (s) 28,6 2416,6
False Positive Rate
• Fast
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• False positives:
– A few vegetation spots
– Some shadows
• False negatives
– Gabled oofs (big slopes)
– Roofless buildings
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• Coarse registration
• Automated (few
parameters)
• Look for closest
comparable shape
Before
Image
Building A
After
Image
Building
A
Damaged
Building
B
Damaged
Building
C
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• Shape deformation characteristics
– Roof is displaced
– Building appears larger because of
adjacent debris
• Texture changes
– Cracks appear on roofs
– Debris is formed on shape
• Precise zones to work with = lower
processing time
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City block damage evaluation
More precise damage evaluation
Extracted from SERTIT map produced January 18 2010
©SERTIT, GeoEye, DLR – map produced by SERTIT
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• Remote sensing CAN help during disaster
response
• Fast object-based processing IS useful for
building and damage detection
• How can it all get better?
– Multi-core processing (CPU, GPU)
– Availability of more spaceborne sensors in the
coming years
– User feedback!
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6/23/12
Québec, 14-17 May 2012
Global Geospatial Conference 2012
Thank you!
References
[1] Becking, Ian. 2004. « How space can better support emergency management and critical
infrastructure protection ». In Proceedings of the Envisat & ERS Sympsium. (Salzburg,
Austria, 6-10 September 2004), p. 172-181.
[2] Hussain, Ejaz, et al. 2011. « Building Extraction and Rubble Mapping for City Port-au-Prince
Post-2010 Earthquake with GeoEye-1 Imagery and Lidar Data ». Photogrammetric
Engineering and Remote Sensing, vol. 77, no 10, p. 1011-1023.
[3] Dell’Acqua, Fabio, Diego Aldo Polli. 2011. « Post-event Only VHR Radar Satellite Data for
Automated Damage Assessment: A Study on COSMO/SkyMed and the 2010 Haiti
Earthquake ». Photogrammetric Engineering and Remote Sensing, vol. 77, no 10, p.
1037-1043.
[4] Monasse, Pascal, Frederic Guichard. 2000. « Fast computation of a contrast-invariant image
representation ». IEEE Transactions on Image Processing, vol. 9, no 5, p. 860-872.
[5] Bin, Luo, J. F. Aujol, Y. Gousseau, S. Ladjal, H. Maitre. 2007. « Resolution- independent
characteristic scale dedicated to satellite images ». IEEE Transactions on Image
Processing, vol. 16, no 10, p. 2503-2514.
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