InSAR Phase Denoising: A Review of Current Technologies and Future Directions

TL;DR

This paper reviews current InSAR phase denoising techniques, categorizing them into traditional, nonlocal, and advanced methods, and compares their performances to guide future research in remote sensing applications.

Contribution

It provides a comprehensive classification and comparison of InSAR phase denoising algorithms, highlighting emerging methods and offering insights for future development.

Findings

Nonlocal filters show outstanding performance.

Numerical experiments compare traditional and advanced methods.

The review offers guidelines for future InSAR signal processing research.

Abstract

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.