Contextual Filtering Methods based on the Subbands and Subspaces Decomposition of Complex SAR Interferograms

Different forms of phase noise in SAR interferograms hamper the accuracy and reliability of interferometric results (InSAR and DInSAR) for the reconstruction of altimetric information and its variations. Geometric and temporal decorrelations, changes in the properties of observed surfaces and delays introduced by the atmospheric layers are the main sources of interferometric noise. In order to reduce their effects and thus increase the robustness of the phase unwrapping algorithms, filtering methods are applied at different levels of the InSAR and DInSAR processes. In this article, we propose an interferometric filtering process that combines wavelet decomposition into subbands and nonlinear weighting filter called spectral contextual filter (SCF). SCF is based on the Goldstein algorithm, whose nonlinear filter coefficients are calculated, for each element of window according to the filter's punctual parameter. This parameter is determined by estimating the adaptive coherent pseudocorrelation of overlapping blocks of the interferometric phase which combines coherence and pseudocorrelation that are both weighted by the coefficient of variation of each block. In order to show the efficiency and robustness of this process, we compared it with the subspaces decomposition filtering process that we implemented. An assessment scheme was led on the basis of a compromise between residues reduction and fringe boundaries conservation. This assessment was carried relative to the global and local parameters. The tests were conducted on complex interferograms acquired on regions with high topographical variations.