Effective Denoising of InSAR Phase Images via Compressive Sensing

Abstract

Interferometric synthetic aperture radar (InSAR) denoising 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 of various final products of InSAR. To address this issue, we develop a compressive sensing (CS)-based InSAR phase denoising technique in this article. Since the spectrum of the InSAR phase image is usually sparse in the 2-D frequency domain, the estimation of sensing dictionary matrix of the 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 the 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 the 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.

Keywords

• Compressive sensing (CS)
• interferometric synthetic aperture radar (InSAR)
• phase filtering
• sparse signal processing
• synthetic aperture radar (SAR)