Loading…

Joint Probability Integral Method and TCPInSAR for Monitoring Mining Time-Series Deformation

Because of the high vegetation coverage, fast deformation in certain mine areas, some SAR interferograms are seriously incoherent. When using time-series synthetic aperture radar interferometry (InSAR) to monitor the surface movement basin of the mining area, there may be a certain period of missing...

Full description

Saved in:
Bibliographic Details
Published in:Journal of the Indian Society of Remote Sensing 2019-01, Vol.47 (1), p.63-75
Main Authors: Zheng, Meinan, Deng, Kazhong, Du, Sen, Liu, Jie, Liu, Jiuli, Feng, Jun
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c316t-15376a10668e1e0349fadb314238e2f11a6e5ea497bfd113f18e10e8251981e13
cites cdi_FETCH-LOGICAL-c316t-15376a10668e1e0349fadb314238e2f11a6e5ea497bfd113f18e10e8251981e13
container_end_page 75
container_issue 1
container_start_page 63
container_title Journal of the Indian Society of Remote Sensing
container_volume 47
creator Zheng, Meinan
Deng, Kazhong
Du, Sen
Liu, Jie
Liu, Jiuli
Feng, Jun
description Because of the high vegetation coverage, fast deformation in certain mine areas, some SAR interferograms are seriously incoherent. When using time-series synthetic aperture radar interferometry (InSAR) to monitor the surface movement basin of the mining area, there may be a certain period of missing deformation information, making the obtained surface time-series deformation incomplete. To this end, this paper proposes a way of using the results predicted by probability integral method (PIM) to replacing the monitoring results that cannot be obtained because of the seriously incoherent SAR interferograms; then, the monitoring results of the high-coherence SAR interferograms and the results predicted by PIM are used by the improved temporarily coherent point SAR interferometry (TCPInSAR) to invert the deformation, thereby obtaining a complete mining time-series deformation. The TCPInSAR using a linear model does not reflect the complex deformation characteristics of the mining area. So this paper focus on the characteristics of deformation of study area, the original linear model is changed to a polynomial model, which improves the applicability of TCPInSAR to monitoring mine deformation. Comparison between the experimental results and levelling shows that the root mean square error (RMSE) and the maximum deviation (MD) of the results obtained by combining the PIM with the improved TCPInSAR are 14.2 mm and 43.0 mm, respectively. Compared with the results obtained by combining the PIM with the TCPInSAR (RMSE = 16.2 mm, MD = 57.5 mm) and the results of using only the TCPInSAR (RMSE = 26.5 mm, MD = 88.4 mm), the monitoring accuracy is increased by 12.3% and 46.4%, respectively.
doi_str_mv 10.1007/s12524-018-0867-y
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2176778336</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2176778336</sourcerecordid><originalsourceid>FETCH-LOGICAL-c316t-15376a10668e1e0349fadb314238e2f11a6e5ea497bfd113f18e10e8251981e13</originalsourceid><addsrcrecordid>eNp1kE9LAzEQxRdRsFY_gLeA52gm2c1uj6X-q7RYbAUPQsh2Z2tKm9QkPey3d5cVPHl6w_B7b5iXJNfAboGx_C4Az3hKGRSUFTKnzUkyYKM8pYIxedrOPMuolOzjPLkIYdsu0wz4IPl8ccZGsvCu1KXZmdiQqY248XpH5hi_XEW0rchqspja5fiN1M6TubMmOm_shsyN7WRl9kiX6A0Gco8ts9fROHuZnNV6F_DqV4fJ--PDavJMZ69P08l4RtcCZKSQiVxqYFIWCMhEOqp1VQpIuSiQ1wBaYoY6HeVlXQGIGlqOYcEzGBWAIIbJTZ978O77iCGqrTt6255UHHKZ54UQsqWgp9beheCxVgdv9to3CpjqSlR9iaotUXUlqqb18N4TDt2_6P-S_zf9AE5idBQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2176778336</pqid></control><display><type>article</type><title>Joint Probability Integral Method and TCPInSAR for Monitoring Mining Time-Series Deformation</title><source>Springer Link</source><source>Alma/SFX Local Collection</source><creator>Zheng, Meinan ; Deng, Kazhong ; Du, Sen ; Liu, Jie ; Liu, Jiuli ; Feng, Jun</creator><creatorcontrib>Zheng, Meinan ; Deng, Kazhong ; Du, Sen ; Liu, Jie ; Liu, Jiuli ; Feng, Jun</creatorcontrib><description>Because of the high vegetation coverage, fast deformation in certain mine areas, some SAR interferograms are seriously incoherent. When using time-series synthetic aperture radar interferometry (InSAR) to monitor the surface movement basin of the mining area, there may be a certain period of missing deformation information, making the obtained surface time-series deformation incomplete. To this end, this paper proposes a way of using the results predicted by probability integral method (PIM) to replacing the monitoring results that cannot be obtained because of the seriously incoherent SAR interferograms; then, the monitoring results of the high-coherence SAR interferograms and the results predicted by PIM are used by the improved temporarily coherent point SAR interferometry (TCPInSAR) to invert the deformation, thereby obtaining a complete mining time-series deformation. The TCPInSAR using a linear model does not reflect the complex deformation characteristics of the mining area. So this paper focus on the characteristics of deformation of study area, the original linear model is changed to a polynomial model, which improves the applicability of TCPInSAR to monitoring mine deformation. Comparison between the experimental results and levelling shows that the root mean square error (RMSE) and the maximum deviation (MD) of the results obtained by combining the PIM with the improved TCPInSAR are 14.2 mm and 43.0 mm, respectively. Compared with the results obtained by combining the PIM with the TCPInSAR (RMSE = 16.2 mm, MD = 57.5 mm) and the results of using only the TCPInSAR (RMSE = 26.5 mm, MD = 88.4 mm), the monitoring accuracy is increased by 12.3% and 46.4%, respectively.</description><identifier>ISSN: 0255-660X</identifier><identifier>EISSN: 0974-3006</identifier><identifier>DOI: 10.1007/s12524-018-0867-y</identifier><language>eng</language><publisher>New Delhi: Springer India</publisher><subject>Deformation ; Earth and Environmental Science ; Earth Sciences ; Integrals ; Interferometric synthetic aperture radar ; Interferometry ; Mathematical models ; Mining ; Monitoring ; Polynomials ; Powder injection molding ; Remote Sensing/Photogrammetry ; Research Article ; Root-mean-square errors ; Synthetic aperture radar ; Time series</subject><ispartof>Journal of the Indian Society of Remote Sensing, 2019-01, Vol.47 (1), p.63-75</ispartof><rights>Indian Society of Remote Sensing 2018</rights><rights>Copyright Springer Nature B.V. 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-15376a10668e1e0349fadb314238e2f11a6e5ea497bfd113f18e10e8251981e13</citedby><cites>FETCH-LOGICAL-c316t-15376a10668e1e0349fadb314238e2f11a6e5ea497bfd113f18e10e8251981e13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Zheng, Meinan</creatorcontrib><creatorcontrib>Deng, Kazhong</creatorcontrib><creatorcontrib>Du, Sen</creatorcontrib><creatorcontrib>Liu, Jie</creatorcontrib><creatorcontrib>Liu, Jiuli</creatorcontrib><creatorcontrib>Feng, Jun</creatorcontrib><title>Joint Probability Integral Method and TCPInSAR for Monitoring Mining Time-Series Deformation</title><title>Journal of the Indian Society of Remote Sensing</title><addtitle>J Indian Soc Remote Sens</addtitle><description>Because of the high vegetation coverage, fast deformation in certain mine areas, some SAR interferograms are seriously incoherent. When using time-series synthetic aperture radar interferometry (InSAR) to monitor the surface movement basin of the mining area, there may be a certain period of missing deformation information, making the obtained surface time-series deformation incomplete. To this end, this paper proposes a way of using the results predicted by probability integral method (PIM) to replacing the monitoring results that cannot be obtained because of the seriously incoherent SAR interferograms; then, the monitoring results of the high-coherence SAR interferograms and the results predicted by PIM are used by the improved temporarily coherent point SAR interferometry (TCPInSAR) to invert the deformation, thereby obtaining a complete mining time-series deformation. The TCPInSAR using a linear model does not reflect the complex deformation characteristics of the mining area. So this paper focus on the characteristics of deformation of study area, the original linear model is changed to a polynomial model, which improves the applicability of TCPInSAR to monitoring mine deformation. Comparison between the experimental results and levelling shows that the root mean square error (RMSE) and the maximum deviation (MD) of the results obtained by combining the PIM with the improved TCPInSAR are 14.2 mm and 43.0 mm, respectively. Compared with the results obtained by combining the PIM with the TCPInSAR (RMSE = 16.2 mm, MD = 57.5 mm) and the results of using only the TCPInSAR (RMSE = 26.5 mm, MD = 88.4 mm), the monitoring accuracy is increased by 12.3% and 46.4%, respectively.</description><subject>Deformation</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Integrals</subject><subject>Interferometric synthetic aperture radar</subject><subject>Interferometry</subject><subject>Mathematical models</subject><subject>Mining</subject><subject>Monitoring</subject><subject>Polynomials</subject><subject>Powder injection molding</subject><subject>Remote Sensing/Photogrammetry</subject><subject>Research Article</subject><subject>Root-mean-square errors</subject><subject>Synthetic aperture radar</subject><subject>Time series</subject><issn>0255-660X</issn><issn>0974-3006</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp1kE9LAzEQxRdRsFY_gLeA52gm2c1uj6X-q7RYbAUPQsh2Z2tKm9QkPey3d5cVPHl6w_B7b5iXJNfAboGx_C4Az3hKGRSUFTKnzUkyYKM8pYIxedrOPMuolOzjPLkIYdsu0wz4IPl8ccZGsvCu1KXZmdiQqY248XpH5hi_XEW0rchqspja5fiN1M6TubMmOm_shsyN7WRl9kiX6A0Gco8ts9fROHuZnNV6F_DqV4fJ--PDavJMZ69P08l4RtcCZKSQiVxqYFIWCMhEOqp1VQpIuSiQ1wBaYoY6HeVlXQGIGlqOYcEzGBWAIIbJTZ978O77iCGqrTt6255UHHKZ54UQsqWgp9beheCxVgdv9to3CpjqSlR9iaotUXUlqqb18N4TDt2_6P-S_zf9AE5idBQ</recordid><startdate>20190101</startdate><enddate>20190101</enddate><creator>Zheng, Meinan</creator><creator>Deng, Kazhong</creator><creator>Du, Sen</creator><creator>Liu, Jie</creator><creator>Liu, Jiuli</creator><creator>Feng, Jun</creator><general>Springer India</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20190101</creationdate><title>Joint Probability Integral Method and TCPInSAR for Monitoring Mining Time-Series Deformation</title><author>Zheng, Meinan ; Deng, Kazhong ; Du, Sen ; Liu, Jie ; Liu, Jiuli ; Feng, Jun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-15376a10668e1e0349fadb314238e2f11a6e5ea497bfd113f18e10e8251981e13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Deformation</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Integrals</topic><topic>Interferometric synthetic aperture radar</topic><topic>Interferometry</topic><topic>Mathematical models</topic><topic>Mining</topic><topic>Monitoring</topic><topic>Polynomials</topic><topic>Powder injection molding</topic><topic>Remote Sensing/Photogrammetry</topic><topic>Research Article</topic><topic>Root-mean-square errors</topic><topic>Synthetic aperture radar</topic><topic>Time series</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zheng, Meinan</creatorcontrib><creatorcontrib>Deng, Kazhong</creatorcontrib><creatorcontrib>Du, Sen</creatorcontrib><creatorcontrib>Liu, Jie</creatorcontrib><creatorcontrib>Liu, Jiuli</creatorcontrib><creatorcontrib>Feng, Jun</creatorcontrib><collection>CrossRef</collection><jtitle>Journal of the Indian Society of Remote Sensing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zheng, Meinan</au><au>Deng, Kazhong</au><au>Du, Sen</au><au>Liu, Jie</au><au>Liu, Jiuli</au><au>Feng, Jun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Joint Probability Integral Method and TCPInSAR for Monitoring Mining Time-Series Deformation</atitle><jtitle>Journal of the Indian Society of Remote Sensing</jtitle><stitle>J Indian Soc Remote Sens</stitle><date>2019-01-01</date><risdate>2019</risdate><volume>47</volume><issue>1</issue><spage>63</spage><epage>75</epage><pages>63-75</pages><issn>0255-660X</issn><eissn>0974-3006</eissn><abstract>Because of the high vegetation coverage, fast deformation in certain mine areas, some SAR interferograms are seriously incoherent. When using time-series synthetic aperture radar interferometry (InSAR) to monitor the surface movement basin of the mining area, there may be a certain period of missing deformation information, making the obtained surface time-series deformation incomplete. To this end, this paper proposes a way of using the results predicted by probability integral method (PIM) to replacing the monitoring results that cannot be obtained because of the seriously incoherent SAR interferograms; then, the monitoring results of the high-coherence SAR interferograms and the results predicted by PIM are used by the improved temporarily coherent point SAR interferometry (TCPInSAR) to invert the deformation, thereby obtaining a complete mining time-series deformation. The TCPInSAR using a linear model does not reflect the complex deformation characteristics of the mining area. So this paper focus on the characteristics of deformation of study area, the original linear model is changed to a polynomial model, which improves the applicability of TCPInSAR to monitoring mine deformation. Comparison between the experimental results and levelling shows that the root mean square error (RMSE) and the maximum deviation (MD) of the results obtained by combining the PIM with the improved TCPInSAR are 14.2 mm and 43.0 mm, respectively. Compared with the results obtained by combining the PIM with the TCPInSAR (RMSE = 16.2 mm, MD = 57.5 mm) and the results of using only the TCPInSAR (RMSE = 26.5 mm, MD = 88.4 mm), the monitoring accuracy is increased by 12.3% and 46.4%, respectively.</abstract><cop>New Delhi</cop><pub>Springer India</pub><doi>10.1007/s12524-018-0867-y</doi><tpages>13</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0255-660X
ispartof Journal of the Indian Society of Remote Sensing, 2019-01, Vol.47 (1), p.63-75
issn 0255-660X
0974-3006
language eng
recordid cdi_proquest_journals_2176778336
source Springer Link; Alma/SFX Local Collection
subjects Deformation
Earth and Environmental Science
Earth Sciences
Integrals
Interferometric synthetic aperture radar
Interferometry
Mathematical models
Mining
Monitoring
Polynomials
Powder injection molding
Remote Sensing/Photogrammetry
Research Article
Root-mean-square errors
Synthetic aperture radar
Time series
title Joint Probability Integral Method and TCPInSAR for Monitoring Mining Time-Series Deformation
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T05%3A55%3A45IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Joint%20Probability%20Integral%20Method%20and%20TCPInSAR%20for%20Monitoring%20Mining%20Time-Series%20Deformation&rft.jtitle=Journal%20of%20the%20Indian%20Society%20of%20Remote%20Sensing&rft.au=Zheng,%20Meinan&rft.date=2019-01-01&rft.volume=47&rft.issue=1&rft.spage=63&rft.epage=75&rft.pages=63-75&rft.issn=0255-660X&rft.eissn=0974-3006&rft_id=info:doi/10.1007/s12524-018-0867-y&rft_dat=%3Cproquest_cross%3E2176778336%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c316t-15376a10668e1e0349fadb314238e2f11a6e5ea497bfd113f18e10e8251981e13%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2176778336&rft_id=info:pmid/&rfr_iscdi=true