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dc.contributor.authorDuan, Meng
dc.contributor.authorXu, Bing
dc.contributor.authorLi, Zhi Wei
dc.contributor.authorWu, Wenhao
dc.contributor.authorCao, Yunmeng
dc.contributor.authorLiu, Jihong
dc.contributor.authorWang, Guanya
dc.contributor.authorHou, Jingxin
dc.date.accessioned2020-09-10T11:13:05Z
dc.date.available2020-09-10T11:13:05Z
dc.date.issued2020-08-09
dc.date.submitted2020-06-23
dc.identifier.citationDuan, M., Xu, B., Li, Z., Wu, W., Cao, Y., Liu, J., … Hou, J. (2020). A New Weighting Method by Considering the Physical Characteristics of Atmospheric Turbulence and Decorrelation Noise in SBAS-InSAR. Remote Sensing, 12(16), 2557. doi:10.3390/rs12162557
dc.identifier.issn2072-4292
dc.identifier.doi10.3390/RS12162557
dc.identifier.urihttp://hdl.handle.net/10754/665060
dc.description.abstractTime series of ground subsidence can not only be used to describe motion produced by various anthropocentric and natural process but also to better understand the processes and mechanisms of geohazards and to formulate effective protective measures. For high-accuracy measurement of small baseline subset interferometric synthetic aperture radar (SBAS-InSAR), atmospheric turbulence and decorrelation noise are regarded as random variables and cannot be accurately estimated by a deterministic model when large spatio-temporal variability presents itself. Various weighting methods have been proposed and improved continuously to reduce the effects of these two parts and provide uncertainty information of the estimated parameters, simultaneously. Network-based variance-covariance estimation (NVCE) and graph theory (GT) are the two main weighting methods which were developed on the basis of previous algorithms. However, the NVCE weighting method only focuses on the influence of atmospheric turbulence and neglects the decorrelation noise. The GT method weights each interferogram in a time series by using the Laplace transformation. Although simple to implement, it is not reasonable to have an equal weight for each pixel in the same interferogram. To avoid these limitations, this study presents a new weighting method by considering the physical characteristics of atmospheric turbulence and decorrelation noise in SBAS-InSAR images. The effectiveness of the proposed method was tested and validated by using a set of simulated experiments and a case study on a Hawaiian island. According to the GPS-derived displacements, the average RMSE of the results from the new weighting method was 1.66 cm, indicating about an 8% improvement compared with 1.79, 1.80 and 1.80 cm from the unweighted method, the NVCE method and the GT method, respectively.
dc.description.sponsorshipThis research was funded by the National Science Fund for Distinguished Young Scholars, grant number 41925016; the National Key R&D Program of China, grant number 2018YFC1503603; the National Natural Science Foundation of China, grant number 41804008; and the Leading Talents Plan of Central South University, grant number 506030101. The Sentinel-1 data were supplied by the European Space Agency and Global Positioning System data were downloaded from the Nevada geodetic Laboratory.
dc.publisherMDPI AG
dc.relation.urlhttps://www.mdpi.com/2072-4292/12/16/2557
dc.rightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleA new weighting method by considering the physical characteristics of atmospheric turbulence and decorrelation noise in SBAS-InSAR
dc.typeArticle
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalRemote Sensing
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionSchool of Geoscience and Info-Physics, Central South University, Changsha 410083, China
dc.contributor.institutionHunan Province Key Laboratory of Coal Resources Clean-utilization and Mine Environment Protection, Hunan Univeristy and Technology, Xiangtan 411202, China
dc.identifier.volume12
dc.identifier.issue16
dc.identifier.pages2557
kaust.personCao, Yunmeng
dc.date.accepted2020-08-03
dc.identifier.eid2-s2.0-85090092782
refterms.dateFOA2020-09-10T11:14:15Z


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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
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