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      InSAR Baseline Estimation for Gaofen-3 Real-Time DEM Generation

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          Abstract

          For Interferometry Synthetic Aperture Radar (InSAR), the normal baseline is one of the main factors that affects the accuracy of the ground elevation. For Gaofen-3 (GF-3) InSAR processing, the poor accuracy of the real-time orbit determination results in a large baseline error, leads to a modulation error in azimuth and a slope error in the range for timely Digital Elevation Model (DEM) generation. In order to address this problem, a novel baseline estimation approach based on Shuttle Radar Topography Mission (SRTM) DEM is proposed in this paper. Firstly, the orbit fitting is executed to remove the non-linear error factor, which is different from traditional methods. Secondly, the height errors are obtained in a slant-range plane between SRTM DEM and the GF-3 generated DEM, which can be used to estimate the baseline error with a linear variation. Then, the real-time orbit can be calibrated by the baseline error. Finally, the DEM generation is performed by using the modified baseline and orbit. This approach has the merit of spatial and precise orbital free ability. Based on the results of GF-3 interferometric SAR data for Hebei, the effectiveness of the proposed algorithm is verified and the accuracy of GF-3 real-time DEM products can be improved extensively.

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          Review Article SAR interferometry—issues, techniques, applications

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            Edgelist phase unwrapping algorithm for time series InSAR analysis.

            We present here a new integer programming formulation for phase unwrapping of multidimensional data. Phase unwrapping is a key problem in many coherent imaging systems, including time series synthetic aperture radar interferometry (InSAR), with two spatial and one temporal data dimensions. The minimum cost flow (MCF) [IEEE Trans. Geosci. Remote Sens. 36, 813 (1998)] phase unwrapping algorithm describes a global cost minimization problem involving flow between phase residues computed over closed loops. Here we replace closed loops by reliable edges as the basic construct, thus leading to the name "edgelist." Our algorithm has several advantages over current methods-it simplifies the representation of multidimensional phase unwrapping, it incorporates data from external sources, such as GPS, where available to better constrain the unwrapped solution, and it treats regularly sampled or sparsely sampled data alike. It thus is particularly applicable to time series InSAR, where data are often irregularly spaced in time and individual interferograms can be corrupted with large decorrelated regions. We show that, similar to the MCF network problem, the edgelist formulation also exhibits total unimodularity, which enables us to solve the integer program problem by using efficient linear programming tools. We apply our method to a persistent scatterer-InSAR data set from the creeping section of the Central San Andreas Fault and find that the average creep rate of 22 mm/Yr is constant within 3 mm/Yr over 1992-2004 but varies systematically with ground location, with a slightly higher rate in 1992-1998 than in 1999-2003.
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              On Newton-Iterative Methods for the Solution of Systems of Nonlinear Equations

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                04 July 2018
                July 2018
                : 18
                : 7
                : 2152
                Affiliations
                [1 ]National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China; luhuanohyeah@ 123456126.com (H.L.); lzf@ 123456xidian.edu.cn (Z.L.); jerryxie9@ 123456gmail.com (J.X.)
                [2 ]Institute of Space-Terrestrial Intelligent Networks (ISTIN) Group, Nanjing University, Nanjing 210023, China; jackokie@ 123456gmail.com
                [3 ]Beijing Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, China; zhangqj@ 123456cast.cn
                Author notes
                [* ]Correspondence: zysuo@ 123456xidian.edu.cn ; Tel.: +86-29-8820-2248
                Author information
                https://orcid.org/0000-0003-0465-2615
                https://orcid.org/0000-0003-2718-0446
                https://orcid.org/0000-0002-5806-992X
                Article
                sensors-18-02152
                10.3390/s18072152
                6069473
                29973543
                ec547a88-7c12-46d5-bf4b-9c0acb3c4224
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 25 May 2018
                : 01 July 2018
                Categories
                Article

                Biomedical engineering
                gf-3,insar,dem,baseline estimation,real-time orbit
                Biomedical engineering
                gf-3, insar, dem, baseline estimation, real-time orbit

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