Phase estimation of distributed scatterer based on singular value decom-position
The covariance matrix is the basis for estimating the phase of distributed scatterer(DS)when using conventional al-gorithm.Therefore,a full combination of SAR data should be generated firstly to construct the sample covariance matrix(SCM).However,this process is not only computationally expensive but also consumes a large amount of storage space.In this paper,a fast algorithm,referred to as SVDI(SVD to interferometric phase matrix),for estimating the phase of DS based on singular value decomposition is proposed.SVDI estimates the phase of DS from the interferometric phase matrix constructed by single-master interferograms rather than the SCM constructed by multi-master interferograms(i.e.,the full combination of SAR data).Therefore,SVDI can effectively improve the computationally efficient and save the storage space.Moreover,it is theoretically proved that the results of SVDI are consistent with the conventional eigenvalue decomposition(EVD)method based on an assumption.The simulated and real SAR data is used to verify the feasibility and reliability of SVDI.The experi-mental results show that the phase and deformation estimation accuracy of SVDI is consistent with that of the conventional method.
distributed scattererphase estimationsample covariance matrixeigen value decompositionsingular value de-composition
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