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

Chandrakanta Ojha; Adele Fusco; Innocenzo M Pinto

doi:10.3390/s19122684

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

Sensors (Basel). 2019 Jun 14;19(12):2684. doi: 10.3390/s19122684.

Authors

Chandrakanta Ojha¹, Adele Fusco^{2

3}, Innocenzo M Pinto^{4

5}

Affiliations

¹ School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85281, USA. cojha1@asu.edu.
² CNR IREA, Via Diocleziano 328, 80124 Naples, Italy. fusco.a@irea.cnr.it.
³ Università del Sannio, Palazzo Dell'Aquila Bosco Lucarelli, Corso Garibaldi, 107 82100 Benevento, Italy. fusco.a@irea.cnr.it.
⁴ Università del Sannio, Palazzo Dell'Aquila Bosco Lucarelli, Corso Garibaldi, 107 82100 Benevento, Italy. pinto@sa.infn.it.
⁵ National Institute for Nuclear Physics, Department of Naples, Strada Comunale Cinthia, 80126 Naples, Italy. pinto@sa.infn.it.

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 [ - π , π ] , 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.

Keywords: ProK-SVD, proximity operator; SAR (Synthetic Aperture Radar) interferograms; dictionary learning; l0-minimization; simulated data; sparse representation.