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Published byMelinda Hancock Modified over 9 years ago
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Co-Registration of SAR Image Pairs for Interferometry
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Current Progress & Preliminary Results
An InSAR Co-registration Module “PurSAR” InSAR co-registration Coherence Evaluation Interferogram Generation DEM Processed by ASF SAR Processor + “PurSAR” + ERDAS Experiments & Analysis
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Co-registration Module “PurSAR”
Coarse co-registration Cross-correlation by FFT Finding coarse tie points Coarse image shift Fine co-registration Finding and filtering sub-pixel tie points 4 and 6 parameters transformation Nearest neighbor, linear, cubic, and SINC interpolators Coherence computation Interferogram generation
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Coarse co-registration
Defining the grids Cross-correlation computation Filtering cross-correlation peaks by peak-to-rms ratio Finding the matching points and discarding the outliers Determining the x and y shifts Shift of the slave image
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Fine co-registration Defining the grids
SINC up-sampling the small windows surrounding the grid points Cross-correlation computation Filtering cross-correlation peaks by peak-to-rms ratio Finding the matching points and discarding the outliers Set 4-par & 6-par transformation equations by least square Re-sampling the slave image by nearest neighbor, linear, cubic, and SINC interpolators SINC interpolators: normalized, windowed, and doppler centroid shifted
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Coherence, Interferogram, and DEM
Coherence evaluation Coherence image computation Coherence statistics: average, histogram, etc Coherence table: re-sampling algorithms vs. coherence magnitude Coherence comparison with ASF SAR Processor Interferogram computation DEM generation Importing co-registered SAR image pair into ERDAS Type in the orbit information Processing the SAR images into DEM in ERDAS Radar DEM evaluation
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Experiments Comparison among airborne RTV SAR DEM, LIDAR and Aerial DEM ERS InSAR processing and coherence evaluation
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Patch 2-AR-Roof
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Armory (Building/Roof)
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Armory-EW (Building/Roof)
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Armory-NS (Building/Roof)
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ERS InSAR processing and coherence evaluation
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ERS Data description InSAR pair: a.cpx (master) and b.cpx (slave)
Location: Fairbanks, Alaska Size: 5000 rows and 1000 columns; cut from a standard scene (about 25000x5000) Format: Single-look complex Processed by ASF SAR Processor The right image is only the magnitude for a.cpx
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Coarse Co-registration
Pair: a.cpx and b.cpx Grids: 5x3 = 15 points Peak-to-RMS Ratio = 0.004 Searching window size = 256 Use only magnitude as input for cross-correlation computation All 15 points have good peaks and no outlier Shifts: 2 in x-direction (range); 1 in y-direction (azimuth) b.cpx b_shift.cpx
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15 Pairs of Matching Points
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Cross-correlation Peak for Point (700, 300)
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Fine Co-registration Pair: a.cpx and b_shift.cpx
Grids: 5x5 = 25 points for better performance Up-sampling ratio = 11 Up-sampling interpolator: SINC Peak-to-RMS Ratio = 0.003 Searching window size = 33 before up-sampling Cross-correlation with only magnitude Cross-correlation with complex data
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Cross-correlation with Only Magnitude ---- Matching Points
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Cross-correlation with Only Magnitude ---- Point (500, 100)
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Cross-correlation Section with Only Magnitude ---- Point (500, 100)
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Cross-correlation with Only Magnitude
All 25 points have good peaks A beautiful sub-pixel peak for Point (500, 100) Azimuth direction section Solid lines Up-sampled cross-correlation 1/11th sub-pixel matching accuracy Dash lines Original pixel cross-correlation Mostly zero after coarse co-registration
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Cross-correlation with Complex Data ---- Point (500, 100)
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Cross-correlation with Complex Data
Only 3 points passed the filter No obvious single peak Noise added by including phase data So better to use only magnitude for sub-pixel co-registration for this SAR pair; but not always
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6-par Transformation
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Discarding the Outliers
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6-par Transformation & Discarding the Outliers
5 outliers were filtered out 6-parameter transformation equations Coefficients for y are much less significant than those for x So 4-parameter transformation is OK NofPoints = 25 X = *x *y StDev of X = Y = *x *y StDev of Y = 20
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4-par Transformation
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Discarding the Outliers
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4-par Transformation & Discarding the Outliers
NofPoints = 25 X = x *x StDev of X = Y = y *x StDev of Y = NofPoints = 20
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2D Separable SINC Function
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Re-sampling Re-sampling the slave image b_shift.cpx by nearest neighbor, linear, cubic, and SINC interpolators b_shift.cpx b_nearest_4par.cpx b_linear_4par.cpx b_cubic_4par.cpx (bicubic) b_sinc2_4par.cpx (SINC length = 2) … b_sinc20_4par.cpx (SINC length = 20)
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Coherence Image ---- a.cpx vs. b.cpx
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Coherence Image ---- a.cpx vs. b_shift.cpx
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Coherence Image ---- a.cpx vs. b_sinc4_4par.cpx
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Coherence Statistics (I)
Image Pair for Coherence Computation Average Coherence a.cpx + b_corr.cpx (Co-registered and Re-sampled by ASF SAR processor) 0.695 a.cpx + b.cpx (before any co-registration) 0.191 a.cpx + b_shift.cpx (after coarse) 0.613 a.cpx + b_nearest_4par.cpx a.cpx + b_linear_4par.cpx 0.661 a.cpx + b_cubic_4par.cpx (bicubic) 0.680
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Coherence Statistics (II)
Image Pair for Coherence Computation Average Coherence a.cpx + b_sinc2_4par.cpx 0.691 a.cpx + b_sinc4_4par.cpx 0.701 a.cpx + b_sinc6_4par.cpx a.cpx + b_sinc8_4par.cpx 0.700 a.cpx + b_sinc10_4par.cpx 0.699 a.cpx + b_sinc14_4par.cpx a.cpx + b_sinc20_4par.cpx 0.696
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Coherence Analysis ASF SAR Processor uses 4 or 6-point cubic convolution for re-sampling It is better than nearest neighbor, linear, and bicubic interpolation However, a 4-point SINC interpolator gives the better coherence than it The speed of computer has increased a lot, and the price has lowered significantly during the last 10 years; So SINC interpolation can be used practically The longer SINC is not always better for interpolation, due to the noise
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Interferogram ---- a.cpx and b_sinc4_4par.cpx
Single look Multi look
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Interferogram ---- Spherical Earth Correction
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Interferogram ---- Phase Unwrapped
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InSAR DEM in Slant Range
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Final InSAR DEM
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