Denoising of full resolution differential SAR interferogram based on K-SVD technique

Ojha, Chandrakanta; Fusco, Adele; Manunta, Michele

doi:10.1109/igarss.2015.7326308

Cited by 4 publications

(1 citation statement)

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“…Yet, very few algorithms for InSAR denoising have been proposed, based on CS [39,40]. In this context, we address the interferometric phase image denoising problem by solving the related noise-free phase estimation problem, using an efficient technique, namely the K-Means Singular Values Decomposition (henceforth K-SVD [34]), capitalizing on sparse representation over trained dictionary [41]. In addition we introduce a proximity concept in applying K-SVD to image-patches, that generally improves its performance at almost no cost.…”

Section: Introductionmentioning

confidence: 99%

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

Ojha

Fusco

Pinto

2019

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%