1. Polarimetric Imaging Sensors for Surveillance Navigation and Communications Howard Schultz and Andrés Corrada-Emmanuel University of Massachusetts, Aerial Imaging and Remote Sensing Laboratory schultz@cs.umass.edu, acorrada@physics.umass.edu Chris Zappa and Michael Banner Columbia University, Lamont-Doherty Earth Observatory zappa@ldeo.columbia.edu, banner@maths.unsw.edu.au Office of Naval Research January 3, 2007

2. Long Term Goals Develop passive remote sensing techniques for studying the dynamics of the upper ocean View the surface environment from a submerged platform –Polarimetric Periscope –Uplink/downlink Communications

3. Optical Flattening

4. Motivation View the above-surface environment from below the surface Objects in a scene taken from underwater are naturally blurred by wave motion –Image sharpening –wave estimation algorithm Wave estimates are not yet accurate enough to substantially improve the reconstructed images above the surface beyond what can be achieved assuming a flat surface.

5. Optical Flattening Use information about the 2D slope field of the ocean surface to remove image distortion -- What would an image taken through the ocean surface look like if there were no waves? Real-time processing

6. Projective Image Formation Model Imaging Array Exposure Center Observation Rays Air Water

7. Optical Flattening Algorithm* Collect polarimetric images Recover the 2D surface slope field Compute the refraction for each rays as it passes through the air-sea interface Create an undistorted image (sort on the direction of the rays in air) *Patent Pending Process, University of Massachusetts, Amherst

8. Degree of Linear Polarization (DoLP) vs. Incidence angle Incidence Angle (degrees) 0 10 20 30 40 50 60 70 80 90 1.0 0.8 0.6 0.4 0.2 0.0 Reflection Refraction

9. Ray tracing image formation model A lens maps incidence angle θ to image position X Lens Imaging Array X θ

10. Ray tracing image formation model A lens maps incidence angle θ to image position X X θ Lens Imaging Array

11. Ray tracing image formation model A lens maps incidence angle θ to image position X X Lens Imaging Array

12. Ray tracing image formation model A lens maps incidence angle θ to image position X X θ Lens Imaging Array

13. Ray tracing image formation model A lens maps incidence angle θ to image position X X θ Lens Imaging Array

14. Refraction Air Water Distorted Image Point

15. Refraction Air Water Distorted Image Point

16. Undistortion Compensating for Refraction Undistorted Image Point Distorted Image Point Air Water Air

17. Undistortion Compensating for Refraction Distorted Image Point Undistorted Image Point Air Water Air

18. Implementation Considerations Uses only one polarimetric camera Exploit the natural time scale separation t sky > t objjects > t waves > t shutter to estimate the polarization distribution of the sky radiance Real-time requires a functional approximation between the inferred incoming Stokes vector, the observed scattered Stokes vector and surface slope (Kattawar, 1994; Voss and Fry, 1984; Sabbah and Shashar, 2006; current effort in RaDyO). Statistical techniques will always be needed to sharpen final image. Requires a precise motion package