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Remote Sens. 2016, 8(10), 830; doi:10.3390/rs8100830

A Sparsity-Based InSAR Phase Denoising Algorithm Using Nonlocal Wavelet Shrinkage

1
Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
2
University of Chinese Academy of Sciences, Beijing 100039, China
3
Department of Geography, Planning and Environment, East Carolina University, Greenville, NC 27858, USA
4
School of Information Science and Engineering, University of Jinan, Jinan 250022, China
5
School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
*
Author to whom correspondence should be addressed.
Academic Editors: Nicolas Baghdadi and Prasad S. Thenkabail
Received: 25 August 2016 / Revised: 28 September 2016 / Accepted: 30 September 2016 / Published: 10 October 2016
View Full-Text   |   Download PDF [18045 KB, uploaded 10 October 2016]   |  

Abstract

An interferometric synthetic aperture radar (InSAR) phase denoising algorithm using the local sparsity of wavelet coefficients and nonlocal similarity of grouped blocks was developed. From the Bayesian perspective, the double- l 1 norm regularization model that enforces the local and nonlocal sparsity constraints was used. Taking advantages of coefficients of the nonlocal similarity between group blocks for the wavelet shrinkage, the proposed algorithm effectively filtered the phase noise. Applying the method to simulated and acquired InSAR data, we obtained satisfactory results. In comparison, the algorithm outperformed several widely-used InSAR phase denoising approaches in terms of the number of residues, root-mean-square errors and other edge preservation indexes. View Full-Text
Keywords: interferometric synthetic aperture radar (InSAR); nonlocal; phase filtering; wavelet shrinkage interferometric synthetic aperture radar (InSAR); nonlocal; phase filtering; wavelet shrinkage
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Fang, D.; Lv, X.; Wang, Y.; Lin, X.; Qian, J. A Sparsity-Based InSAR Phase Denoising Algorithm Using Nonlocal Wavelet Shrinkage. Remote Sens. 2016, 8, 830.

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