IGARSS 2010, Honolulu Motivation Introduction Algorithm Validation Conclusion Iterative Calibration of Relative Platform Position: A New Method for Baseline Estimation Tiangang Yin 1 , Emmanuel Christophe 1 , Soo Chin Liew 1 , Sim Heng Ong 2 1 CENTRE FOR REMOTE I MAGING,SENSING AND PROCESSING 2 DEPT. OF ELECTRICAL AND COMPUTER ENGINEERING, NATIONAL UNIVERSITY OF SINGAPORE
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Iterative calibration of relative platform position a new_method for_baseline_estimation
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We have already knowBaseline precision is significant to the interferometricaccuracyPrecise estimation is required
IdeaInterferometric result can provide information on baselineConcept can be extended under multiple passes condition,from baseline to individual sensor positionIteration and Constraint
Baseline ConceptRefer to the relative distance between two sensorsHighlight “relative”
depends on the chosen master image as coordinate originbuild a coordinate system base on master image position,normally described using “parallel” and “perpendicular”
Initially estimated using orbital information, interpolatedfrom platform position vector
Baseline ErrorThe root of baseline estimation error is the inaccurateplatform position from orbit dataIt can happen on any of the interferometric pairAll the interferograms will be wrong with the sameinaccurate path
Geometrical ConstraintThe geometric representation of multiple platform positionscan be constructed as polygon(2D) or polyhedron(3D)Using the orbit estimated baseline, this geometricrepresentation can be constructed
Baseline CalibrationIn the past method, error of perpendicular baseline can bereduced by using GCP or reference DEMHowever, the correction is only on the relative distance. Noguarantee for the corrected baseline.
From baseline to relative positionWhen more information on platform position can be interpretedfrom data, global constraint of platform position is needed.Without constraint, the geometry of platform positions willbreak.
Because the problem will become very complicated in 3Dwhen more passes are used
An iterative optimization method will be provided undergeometry constraintGlobal baseline calibrationDetection and quantitative calibration of any pass withinaccurate orbit information
Motivation Introduction Algorithm Validation Conclusion Coordinate System Iteration
Transfer Equation: ~Bji ' −~BijIs it valid?
Assumption can be made that all of the platform have thesame direction of ~VImage pixels within one range row will share the samebaseline TCN coordinates
∆θ = arctan
√| ~Bij · c |2 + | ~Bij · t |2
Ai + R(2)
Ai : the platform altitude of image i (691.65 km for ALOS)R: the radius of the earth (6378.1 km)
Motivation Introduction Algorithm Validation Conclusion Coordinate System Iteration
System Error
The baseline component along t is very smallTherefore, for baseline of 1 km along c, the axis error is0.0081◦
the baseline error is ~Bij · c × tan ∆θ ' 14 cm for this systemConclude: TCN coordinates system will be considered atcorresponding point between all passes
Motivation Introduction Algorithm Validation Conclusion Coordinate System Iteration
Iteration: Starting PointK + 1 passes over same areaDifferential interferogram and baseline is generated for allcombinationsProcessed with both baseline vector and baselinechanging rateInitialization:
Motivation Introduction Algorithm Validation Conclusion Coordinate System Iteration
Iteration Steps
Take one pass as master image, calculate the baselineerror to be corrected
Average the result: ∆~P(n)i = 1
K ×∑
j 6=i ∆~Bij
Update all the baseline vectors: ~Bij = ~Bij + ∆~P(n)i
A weight coefficient 1n can be added before ∆~P(n)
i to slow down the convergence
Update the reversed baseline ~Bji
Change another master image and go back to first step,until all of the images have been taken once as masterimageCalculate the total displacement of all platform:∆~P(n) =
∑K +1i=1 | ∆~P(n)
i |Iteration n finished, Take n = n + 1 and restart
Motivation Introduction Algorithm Validation Conclusion Coordinate System Iteration
Iteration Steps
Take one pass as master image, calculate the baselineerror to be correctedAverage the result: ∆~P(n)
i = 1K ×
∑j 6=i ∆~Bij
Update all the baseline vectors: ~Bij = ~Bij + ∆~P(n)i
A weight coefficient 1n can be added before ∆~P(n)
i to slow down the convergence
Update the reversed baseline ~Bji
Change another master image and go back to first step,until all of the images have been taken once as masterimageCalculate the total displacement of all platform:∆~P(n) =
∑K +1i=1 | ∆~P(n)
i |Iteration n finished, Take n = n + 1 and restart
Motivation Introduction Algorithm Validation Conclusion Coordinate System Iteration
Iteration Steps
Take one pass as master image, calculate the baselineerror to be correctedAverage the result: ∆~P(n)
i = 1K ×
∑j 6=i ∆~Bij
Update all the baseline vectors: ~Bij = ~Bij + ∆~P(n)i
A weight coefficient 1n can be added before ∆~P(n)
i to slow down the convergence
Update the reversed baseline ~Bji
Change another master image and go back to first step,until all of the images have been taken once as masterimageCalculate the total displacement of all platform:∆~P(n) =
∑K +1i=1 | ∆~P(n)
i |Iteration n finished, Take n = n + 1 and restart
Motivation Introduction Algorithm Validation Conclusion Coordinate System Iteration
Iteration Steps
Take one pass as master image, calculate the baselineerror to be correctedAverage the result: ∆~P(n)
i = 1K ×
∑j 6=i ∆~Bij
Update all the baseline vectors: ~Bij = ~Bij + ∆~P(n)i
A weight coefficient 1n can be added before ∆~P(n)
i to slow down the convergence
Update the reversed baseline ~Bji
Change another master image and go back to first step,until all of the images have been taken once as masterimageCalculate the total displacement of all platform:∆~P(n) =
∑K +1i=1 | ∆~P(n)
i |Iteration n finished, Take n = n + 1 and restart
Motivation Introduction Algorithm Validation Conclusion Coordinate System Iteration
Iteration Steps
Take one pass as master image, calculate the baselineerror to be correctedAverage the result: ∆~P(n)
i = 1K ×
∑j 6=i ∆~Bij
Update all the baseline vectors: ~Bij = ~Bij + ∆~P(n)i
A weight coefficient 1n can be added before ∆~P(n)
i to slow down the convergence
Update the reversed baseline ~Bji
Change another master image and go back to first step,until all of the images have been taken once as masterimageCalculate the total displacement of all platform:∆~P(n) =
Motivation Introduction Algorithm Validation Conclusion Coordinate System Iteration
Iteration Steps
Take one pass as master image, calculate the baselineerror to be correctedAverage the result: ∆~P(n)
i = 1K ×
∑j 6=i ∆~Bij
Update all the baseline vectors: ~Bij = ~Bij + ∆~P(n)i
A weight coefficient 1n can be added before ∆~P(n)
i to slow down the convergence
Update the reversed baseline ~Bji
Change another master image and go back to first step,until all of the images have been taken once as masterimageCalculate the total displacement of all platform:∆~P(n) =
∑K +1i=1 | ∆~P(n)
i |Iteration n finished, Take n = n + 1 and restart
Data Over Singapore8 passes of PALSAR over the Singapore betweenDecember 2006 and September 2009 are usedSRTM is used as reference DEMGAMMA software is used for the interferogramsPython used for programming