Interferometric SAR Phase Denoising Using Proximity-Based K-SVD Technique.

Chandrakanta Ojha,Adele Fusco,Innocenzo M Pinto

doi:10.3390/s19122684

Abstract

This paper addresses the problem of interferometric noise reduction in Synthetic Aperture Radar (SAR) interferometry based on sparse and redundant representations over a trained dictionary. The idea is to use a Proximity-based K-SVD (ProK-SVD) algorithm on interferometric data for obtaining a suitable dictionary, in order to extract the phase image content effectively. We implemented this strategy on both simulated as well as real interferometric data for the validation of our approach. For synthetic data, three different training dictionaries have been compared, namely, a dictionary extracted from the data, a dictionary obtained by a uniform random distribution in , and a dictionary built from discrete cosine transform. Further, a similar strategy plan has been applied to real interferograms. We used interferometric data of various SAR sensors, including low resolution C-band ERS/ENVISAT, medium L-band ALOS, and high resolution X-band COSMO-SkyMed, all over an area of Mt. Etna, Italy. Both on simulated and real interferometric phase images, the proposed approach shows significant noise reduction within the fringe pattern, without any considerable loss of useful information.

Highlights

Interferometric Synthetic Aperture Radar (InSAR) [1,2,3,4,5,6,7,8,9] is a consolidated remote sensing technique with broad applications in the field of Earth and environmental sciences
Three different training dictionaries have been compared, namely, a dictionary extracted from the data, a dictionary obtained by a uniform random distribution in [−π, π ], and a dictionary built from discrete cosine transform
SAR interferograms are affected by several decorrelation effects, depending on different noise sources, which collectively produce interferogram phase noise

Summary

Introduction

Interferometric Synthetic Aperture Radar (InSAR) [1,2,3,4,5,6,7,8,9] is a consolidated remote sensing technique with broad applications in the field of Earth and environmental sciences. SAR interferograms are affected by several decorrelation effects, depending on different noise sources, which collectively produce interferogram phase noise. Decorrelation stems from system noise, processing errors and other internal and external factors (e.g., atmospheric fluctuations) [6,9,11]. Several techniques have been proposed in the literature for getting rid of the decorrelation effects in the interferometric phase noise. Non-adaptive filtering methods, including the mean filtering technique proposed by Rosen [9], are not so effective for InSAR interferograms. The adoption of some fixed windows for filtering can induce phase distortions due to the periodic character of interferograms not being considered

Objectives

Results

Conclusion