InSAR Phase Denoising: A Review of Current Technologies and Future Directions
It offers a guideline for researchers in remote sensing to enhance InSAR processing for topography mapping and deformation monitoring, but it is incremental as a review paper.
This paper provides a comprehensive review of InSAR phase denoising methods, classifying them into categories like traditional local filters, transformed-domain filters, nonlocal filters, and advanced signal processing techniques, and compares their performance using simulated and measured data.
Nowadays, interferometric synthetic aperture radar (InSAR) has been a powerful tool in remote sensing by enhancing the information acquisition. During the InSAR processing, phase denoising of interferogram is a mandatory step for topography mapping and deformation monitoring. Over the last three decades, a large number of effective algorithms have been developed to do efforts on this topic. In this paper, we give a comprehensive overview of InSAR phase denoising methods, classifying the established and emerging algorithms into four main categories. The first two parts refer to the categories of traditional local filters and transformed-domain filters, respectively. The third part focuses on the category of nonlocal (NL) filters, considering their outstanding performances. Latter, some advanced methods based on new concept of signal processing are also introduced to show their potentials in this field. Moreover, several popular phase denoising methods are illustrated and compared by performing the numerical experiments using both simulated and measured data. The purpose of this paper is intended to provide necessary guideline and inspiration to related researchers by promoting the architecture development of InSAR signal processing.