Interferogram denoising using an iteratively refined nonlocal InSAR filter

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ABSTRACTInterferogram denoising is important for height reconstruction and deformation measurement. For high-resolution interferograms over heterogeneous areas, local biases and resolution losses may appear due to the violation of the local stationarity assumption. To address this problem, an iteratively refined nonlocal filter is proposed, whose estimation is refined iteratively by jointly using the amplitude, interferometric phase, coherence, and the pre-estimated patches. For nonlocal interferometric synthetic aperture radar (InSAR) techniques, the similarity of two pixels is computed via their surrounding patches, followed by the maximum likelihood weighted averaging of similar pixels. In this letter, the outliers in the search window is identified before the weight calculation. If the normalized probability density function (NPDF) of the central pixel and other surrounding pixels in the search window is less than the preset threshold, the pixel will be assigned with the minimum weight in the search window. Moreover, the denoising weight is calculated not only depending on the probabilistic patch-based (PPB) similarity, but also the coherence of the pixels in the search window. Both simulated and real data experiments are used to validate the effectiveness of the proposed method.