1. imaginlabs.com Patent U.S. 8,121,433 B2 California Institute of Technology COSI-Corr Automatic Imperviousness Classification Study Cases Sebastien Leprince Francois Ayoub Jiao Lin Jean-Philippe Avouac leprincs@caltech.edu Office: 626-395-2912 Cell: 626-240-9041 California Institute of Technology

2. Case Study: Automatic classification of impervious surfaces Data: GeoEye image, 4-band multispectral, 2m GSD, above Indianapolis, with impervious surface classification benchmark (courtesy of MWH). Worldview 8-band multispectral images, 2m GSD (courtesy of DigitalGlobe): - Image of San Clemente, CA - Image of Sydney, Australia Goal: Testing automatic methods to extract the percentage of impervious surfaces using satellite images. Applications: Better management of storm water run-offs, tax identification.

3. Indianapolis Test Image - GeoEye GeoEye Image

4. Indianapolis Test Image Imperviousness Benchmark Provided Warmer color represents more % impervious

5. Indianapolis Test Image COSI-Corr automatic imperviousness analysis Black is 0% impervious, White is 100% impervious Some inconsistencies exist but land boundaries are well defined. In particular, bare soils are harder to classify. More robustness can be achieved using Worldview-2 8-band multispectral images

6. San Clemente CA, Test Image #1 – Worldview 2

7. COSI-Corr Imperviousness result

8. San Clemente CA, Test Image #2 – Worldview 2

9. COSI-Corr Imperviousness result

10. Sydney, Test Image #1 – Worldview 2

11. COSI-Corr Imperviousness result

12. Sydney, Test Image #2 – Worldview 2

13. COSI-Corr Imperviousness result

14. Sydney, Test Image #3 – Worldview 2

15. COSI-Corr Imperviousness result

16. Sydney, Test Image #4 – Worldview 2

17. COSI-Corr Imperviousness result

18. Conclusions COSI-Corr can provide automatic classification of impervious surfaces. It was found that classification accuracy is improved when using Worldview-2 8-band multispectral images instead of GeoEye 4-band images. The most difficult parts to map are bare soils. Combining images at different seasons should alleviate most problems. More discussion is needed to decided how water bodies should be classified – should we differentiate between swimming- pools and natural water bodies? We could introduce a “no data” class when the classification is not accurate, in particular in shadow areas. COSI-Corr can implement an automatic shadow detection if using Worldview-2 images. More characteristics could be added if coupled with high resolution terrain model, which can also be extracted using COSI-Corr and Worldview stereo imagery (more competitive than LiDAR). The results of this study are preliminary and can be improved. Please contact the authors for more information.