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A New Nonlocal Method for Ground-Based Synthetic Aperture Radar Deformation Monitoring

Lookup NU author(s): Zheng Wang, Professor Zhenhong Li, Professor Jon Mills



This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2018.

For re-use rights please refer to the publisher's terms and conditions.


Ground-based synthetic aperture radar (GBSAR) interferometry offers an effective solution for the monitoring of surface displacements with high precision. However, coherence estimation and phase filtering in GBSAR interferometry is often based on a rectangular window, resulting in estimation biases and resolution loss. To address these issues, conventional nonlocal methods developed for spaceborne synthetic aperture radar are investigated with GBSAR data for the first time. Based on investigation and analysis, an efficient similarity measure is proposed to identify pixels with similar amplitude behaviors and a comprehensive nonlocal method is presented on the basis of this concept with the aim of overcoming current limitations. Pixels with high similarity are identified from a large search window for each point based on a stack of GBSAR single look complex images. Coherence is calculated based on the selected sibling pixels and then enhanced by the second kind statistic estimator. Nonlocal means filtering is also performed based on the sibling pixels to reduce interferometric phase noise. Experiments were conducted using short- and long-term GBSAR interferograms. Qualitative and quantitative analyses of the proposed nonlocal method and other existing techniques demonstrate that the new approach has advantages in terms of coherence estimation and phase filtering capability. The proposed method was integrated into a complete GBSAR small baseline subset algorithm and a time series analysis was achieved for two stacks of data sets. Considered alongside experimental results, this successful application demonstrates the feasibility of the proposed nonlocal method to facilitate the adoption of GBSAR for deformation monitoring applications.

Publication metadata

Author(s): Wang Z, Li Z, Mills JP

Publication type: Article

Publication status: Published

Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2018

Volume: 11

Issue: 10

Pages: 3769-3781

Print publication date: 01/10/2018

Online publication date: 24/08/2018

Acceptance date: 06/08/2018

Date deposited: 12/09/2018

ISSN (print): 1939-1404

ISSN (electronic): 2151-1535

Publisher: IEEE


DOI: 10.1109/JSTARS.2018.2864740


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