A Robust Nonlocal Tensor Decomposition Method for InSAR Phase Denoising

Interferometric synthetic aperture radar (InSAR) images are severely corrupted by noise in both magnitude and phase. It is significantly essential to recover the true interferometric phase during InSAR signal processing. Usually, traditional phase denoising methods are to find homogeneous samples for filtering with the need to balance noise reduction and phase preservation, which may be a problem in dealing with topography scenes. In this paper, a novel algorithm of robust nonlocal tensor decomposition (RNLTD) for InSAR phase denoising is proposed. In the scheme, a nonlocal tensor model of the interferogram is constructed by selecting and stacking similar image patches in a nonlocal region. Benefiting from the simultaneous use of nonlocal and tensor tools, superior low-rank properties of this nonlocal tensor can be acquired, which is also confirmed by numerical analysis. Then, a robust tensor decomposition algorithm is proposed to formulate the low-rank recovery of the interferogram and constrain the sparse outliers for noise reduction. Next, an alternating direction method of multipliers (ADMM) solution is applied to robustly and accurately restore the noise-reduced interferometric phase. As a result, the proposed RNLTD algorithm takes advantage of effectively capturing the phase structure in a high-dimension manner, which is helpful in phase preservation with the achievement of excellent noise reduction. Lastly, the experimental analysis using one set of simulated and two sets of measured InSAR data is performed to show the promising performance of the proposed algorithm.