Effective Denoising of InSAR Phase Images via Compressive Sensing
Minseok Kang, Jaemin Baek
Abstract
Interferometric synthetic aperture radar (InSAR) denosing is an essential processing step in deformation measurement and topography reconstruction. A noisy InSAR phase image gives rise to the phase unwrapping difficulties and even results in the degradation on various final products of InSAR. To address this issue, we develop a compressive sensing (CS)-based InSAR phase denoising technique in this paper. Since the spectrum of InSAR phase image is usually sparse in two-dimensional frequency domain, the estimation of sensing dictionary matrix of linear system between the InSAR phase signal and its spectrum in the pursuit of sparsity is considered for InSAR phase denoising. The optimization problem derived by signal parameterization approach is effectively carried out by estimating the basis function that is closely analogous to the strongest signal component in the spectrum of InSAR phase image. The proposed method is effectively capable of eliminating noise and preserving detailed fringe information of InSAR. In the end, simulations and experimental results demonstrate that the proposed scheme outperforms other conventional InSAR phase denoising methods.