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FV_IGARSS11.ppt

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FV_IGARSS11.ppt

  1. 1. Combining space-borne SAR data and digital camera images to monitor glacier flow by remote and proximal sensing R. Fallourd, F. Vernier ,Y. Yan, D. Rosu, E. Trouvé, J.-M. Nicolas, J.-M. Friedt and L. Moreau ANR-07-MDCO-04
  2. 2. Experimental Site: Mont Blanc <ul><li>Mont blanc valley </li></ul><ul><li>Argentière glacier </li></ul>SAR LOS
  3. 3. Overview <ul><li>The correlation algorithm </li></ul><ul><ul><li>NCC (Normalized Cross Correlation) </li></ul></ul><ul><ul><li>Bases of fast correlation </li></ul></ul><ul><li>Optical data set </li></ul><ul><ul><li>Data & processing </li></ul></ul><ul><ul><li>Results </li></ul></ul><ul><li>SAR data set </li></ul><ul><ul><li>Data & processing </li></ul></ul><ul><ul><li>Results </li></ul></ul><ul><li>Data fusion </li></ul><ul><li>Conclusion & Perspectives </li></ul>
  4. 4. Fast Correlation Technique <ul><li>Texture tracking, looking for the maximum of a similarity function. </li></ul><ul><li>Use the Normalized Cross Correlation. </li></ul><ul><ul><li>Classical function </li></ul></ul><ul><ul><li>Optimized implementation </li></ul></ul><ul><ul><li>Parallel implementation </li></ul></ul><ul><ul><li>Optimization and parallelization can be extended to others similarity functions </li></ul></ul>
  5. 5. Fast Correlation Technique <ul><li>The main objective is to reuse already computed values. </li></ul>A master window centered at the positi on (k,l)
  6. 6. Fast Correlation Technique <ul><li>The main objective is to reuse already computed values. </li></ul><ul><li>Due to a dense correlation, the overlapping of the computation is important. </li></ul>A master window centered at the position (k,l+1) <ul><li>Hatchure part is already computed. </li></ul><ul><li>It is not recomputed for this new position of the master window. </li></ul><ul><li>Sliding vectors or matrices are used to manage the computed data. </li></ul>
  7. 7. Proximal sensor: Camera <ul><li>6 images every day, with 2-hour intervals, from 8:00 AM to 6:00 PM. </li></ul><ul><li>16:9 High Resolution images of 10 Mega pixels (4224 x 2376 pixels) </li></ul>Lognan serac falls <ul><li>Automated digital camera installed near the Argentière glacier. </li></ul><ul><li>6 months without supervision. </li></ul>
  8. 8. Proximal sensor: Camera <ul><li>Processing: </li></ul><ul><ul><li>RGB JPEG images are converted in gray-scale images: </li></ul></ul><ul><li>Luminance = 0.3 x Red + 0.59 x Green + 0.11 x Blue </li></ul><ul><ul><li>An initial co-registration between the images is made on the motion-free part of the images. </li></ul></ul><ul><ul><li>The fast correlation is applied with: </li></ul></ul><ul><ul><ul><li>31 x 31 pixels master window </li></ul></ul></ul><ul><ul><ul><li>51 x 51 pixels slave window </li></ul></ul></ul>
  9. 9. Proximal sensor: Camera <ul><li>Highlight: </li></ul><ul><ul><li>Displacement </li></ul></ul><ul><ul><li>Fallen serac </li></ul></ul><ul><ul><li>Serac that accelerates (will fall) </li></ul></ul>Magnitude of 2D displacement (2008-10-9 / 2008-10-10). Orientation of 2D displacement. (2008-10-9 / 2008-10-10). Lognan serac falls, 2008-10-09.
  10. 10. Remote sensor: SAR <ul><li>35 stripmap TerraSAR-X images on the Mont-Blanc test site: </li></ul><ul><ul><li>Many Ascending/Descending temporal series. </li></ul></ul><ul><ul><li>In polarization HH, HH/VV or HH/HV. </li></ul></ul><ul><ul><li>Incidence angle of 37°. </li></ul></ul><ul><ul><li>1.36 m per pixel in range and 2.04 m per pixel in azimuth. </li></ul></ul><ul><ul><li>Large HR scene (about 30x50 km²). </li></ul></ul><ul><ul><li>380 Mega pixels per image. </li></ul></ul>SAR LOS TS-X amplitude strip-map image 2008-09-29.
  11. 11. Remote sensor: SAR <ul><li>Processing: </li></ul><ul><ul><li>An initial co-registration by a simple translation (without resampling). </li></ul></ul><ul><ul><li>The fast correlation is applied with: </li></ul></ul><ul><ul><ul><li>61 x 61 pixels master window </li></ul></ul></ul><ul><ul><ul><li>77 x 77 pixels slave window </li></ul></ul></ul><ul><ul><ul><li>~16m max. </li></ul></ul></ul><ul><ul><li>A post-processing step can be necessary to deduce the offsets only due to the glacier movement. </li></ul></ul>
  12. 12. Remote sensor: SAR <ul><li>Dense correlation of a whole SAR image </li></ul><ul><li>Each alpine glacier of the area appears </li></ul><ul><li>Others particular structures are enlightened </li></ul>SAR LOS TS-X image 2008-09-29. Magnitude of 2D displacement (2008-09-29 / 2008-10-10).
  13. 13. Remote sensor: SAR <ul><li>Good results in textured areas </li></ul><ul><li>Dis-correlation due to: </li></ul><ul><ul><li>Too many changes ( snowfall, too large movement...). </li></ul></ul><ul><ul><li>Not enough texture. </li></ul></ul>SAR LOS TS-X image 2008-09-29. TS-X image 2008-09-29. 2D displacement magnitude (2008-09-29 / 2008-10-10). 2D displacement orientation (2008-09-29 / 2008-10-10).
  14. 14. Computation time <ul><li>octo-core Intel(R) Core(TM) i7 3GHz </li></ul><ul><li>24GB memory </li></ul>Images Optimisation 1 cpu 8 cpu Optic without 12 days 36 hours with 80 min 10 min Whole SAR without - 18 days with 5 days 15 hours SAR part without 120 hours 12 hours with 4 hours 30 min
  15. 15. Data Fusion
  16. 16. Data Fusion <ul><li>Problem to solve: </li></ul><ul><li>R = PU </li></ul><ul><ul><li>R: 2D displacement vector mesured on each projection. </li></ul></ul><ul><ul><li>P: matrix of projection vectors. </li></ul></ul><ul><ul><li>U: unknown vector of 3D displacement </li></ul></ul><ul><li>WLS solution: </li></ul><ul><ul><li>With the input covariance matrix </li></ul></ul>
  17. 17. Data Fusion <ul><li>Fusion of Optic and SAR displacement </li></ul><ul><li>Displacement speed in meter. </li></ul><ul><li>Small area due to orthogonal point of view between SAR and Optic measurement. </li></ul>3D displacement magnitude
  18. 18. Conclusion & perspectives <ul><li>The computation on each point of the image can be achieved in a reasonable time. </li></ul><ul><li>The optimization deals with optical and SAR images. </li></ul><ul><li>The experiments highlight the problems and results obtained by fusion of these results. </li></ul><ul><li>The software is available in the “EFIDIR Tools” (GPL) www.efidir.fr. </li></ul><ul><li>''Fast Correlation Technique for Glacier Flow Monitoring by Digital Camera and Space-borne SAR Images'' accepted in Journal Image and Video Processing. </li></ul>
  19. 19. Conclusion & perspectives <ul><li>Install two cameras higher to: </li></ul><ul><ul><li>See the top of glacier with the camera. </li></ul></ul><ul><ul><li>Have larger overlapping area observed by satellite and cameras. </li></ul></ul><ul><ul><li>Use the stereo effect to compute 3D displacement with these new cameras. </li></ul></ul><ul><ul><li>Update DEM for the use of SAR images. </li></ul></ul>
  20. 20. Questions...
  21. 22. Experimental Data <ul><li>6 images per day from an automated digital camera installed in the front of ice falls of “Argentière glacier”. </li></ul><ul><ul><li>Data size: 6x10 Mega pixels per day. </li></ul></ul><ul><li>1 TerraSAR-X image every 11 days. </li></ul><ul><ul><li>Data size: more than 380 Mega pixels per image. </li></ul></ul><ul><li>Objectives: </li></ul><ul><ul><li>Compute displacement for both data sets. </li></ul></ul><ul><ul><li>Combine the results to compute a 3D displacement. </li></ul></ul>
  22. 23. Fast Correlation Technique <ul><li>The main technique is based on sliding vector and matrix </li></ul><ul><li>Vector of size 4, 1 st step: </li></ul>The first line The second line The third line <ul><li>Matrix of size 3x3, 1 st step: </li></ul>Head 1 2 3 4 ? ? ? 11 12 13 21 22 23 31 32 33 ? ? 11 12 13 21 22 23 31 32 33 ? ?
  23. 24. Fast Correlation Technique <ul><li>The main technique is based on sliding vector and matrix </li></ul><ul><li>Vector of size 4, 2 nd step: </li></ul><ul><li>Matrix of size 3x3, 2 nd step: </li></ul>The first line The second line The third line Head 1 2 3 4 ? ? ? 11 12 13 21 22 23 31 32 33 ? ? 11 12 13 21 22 23 31 32 33 ? ?
  24. 25. Fast Correlation Technique <ul><li>The main technique is based on sliding vector and matrix </li></ul><ul><li>Vector of size 4, 2 nd step: </li></ul><ul><li>Matrix of size 3x3, 2 nd step: </li></ul>The first line The second line The third line Head 11 12 13 14 22 23 24 32 33 34 ? 11 12 13 14 22 23 24 32 33 34 ? 1 2 3 4 5 ? ?

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