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