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COSI- Corr Automatic Detection of Bombing Carters using Worldview Imagery. Sebastien Leprince Francois Ayoub Jiao Lin Jean-Philippe Avouac leprincs @ caltech.edu Office: 626-395-2912 Cell: 626-240-9041 California Institute of Technology. Patent U.S. 8,121,433 B2

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  1. COSI-Corr Automatic Detection of Bombing Carters using Worldview Imagery SebastienLeprince Francois Ayoub Jiao Lin Jean-Philippe Avouac leprincs@caltech.edu Office: 626-395-2912 Cell: 626-240-9041 California Institute of Technology Patent U.S. 8,121,433 B2 California Institute of Technology imaginlabs.com

  2. Case Study: City of Anadan, Syria Data: Worldview panchromatic image pair, ortho-ready processing level (courtesy of DigitalGlobe): - Image 1 acquired on July 1, 2012 - Image 2 acquired on July 31, 2012 Goal: Comparing images bracketing the battle of Anadan, which took place on July 29th, 2012, between the Free Syrian Army and the Syrian Army. Goal is to quantify the intensity and location of bombing by detecting and counting the craters that were present on July 31st, but not on July 1st. Applications: Amnesty International is documenting attacks against civilians so that those responsible can be help accountable.

  3. Automatic detection of crater patterns between imagery. For this applications, bombing craters are assumed to be dark disks of diameter of about 6 m.

  4. Detection Results on Several Image Areas Detected craters will be displayed as white dots, with dot area proportional to confidence level

  5. Automatic detection of Craters (Before images) – Areas 1/2/3/4

  6. Automatic detection of Craters (After images) – Areas 1/2/3/4

  7. Automatic detection of Craters (New Craters Detected) – Areas 1/2/3/4

  8. Automatic detection of Craters (Before images) – Areas 5/6/7/8

  9. Automatic detection of Craters (After images) – Areas 5/6/7/8

  10. Automatic detection of Craters (New Craters Detected) – Areas 5/6/7/8

  11. Automatic detection of Craters (Before images) – Area 9

  12. Automatic detection of Craters (After images) – Area 9

  13. Automatic detection of Craters (New Craters Detected) – Area 9

  14. A Few Areas Containing Erroneous Detections (False Positive and False Negative)

  15. Automatic detection of Craters (Before images)

  16. Automatic detection of Craters (After images) Bright dots (haystacks?), are detected as craters when the land becomes darker in the later image due to agricultural activities. Combining MS imagery analysis would mitigate false positives. The major crater in the middle of the road will be hardly detected (false negative) because in this preliminary study, typical craters were assumed to have a diameter around 6 m. This crater is significantly larger. Analysis with a varying diameter would remedy this problem.

  17. Automatic detection of Craters (New Craters Detected)

  18. Conclusions • COSI-Corr Automatic crater detection has been implemented, delivering high quality detection when compared to visual interpretation. Would need ground truth to validate results further. • About 130 new craters between July 1st and July 31st were detected in the Anadan region. However, this number might slightly be over-estimated as most detection errors seem to be false positives. • Strong shadows and complex urban features did not impair the automatic detection. • Automatic detection on panchromatic imagery could be completed with information from multi-spectral imagery to reduce the number of false positives. • The results of this study are preliminary and could be improved. They could also be adapted to different detections or purposes. Please contact the authors for more information.

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