Multiple objects automatic detection of GPR data based on the AC-EWV and Genetic Algorithm
The automatic detection of multiple objects in Ground penetrating radar (GPR) data is investigated by searching for the reflected hyperbolas of buried objects, which can reduce the subjectivity of operators and improve the identification accuracy. Based on Frequency-wavenumber (F-K) migration, the a...
Saved in:
| Published in: | IEEE transactions on geoscience and remote sensing Vol. 61; p. 1 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
IEEE
01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 0196-2892, 1558-0644 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | The automatic detection of multiple objects in Ground penetrating radar (GPR) data is investigated by searching for the reflected hyperbolas of buried objects, which can reduce the subjectivity of operators and improve the identification accuracy. Based on Frequency-wavenumber (F-K) migration, the accurate calculation of electromagnetic wave velocity (AC-EWV) is proposed by searching for the minimum image entropy of migrated radargrams. To avoid global searching, potential positions of object hyperbolas are selected from the binarized radargram through the vertical gray gradient searching, then the sub_window is extracted with the potential position as the center. The best fitting hyperbola is detected with the genetic algorithm (GA) in the sub_window, and objects are finally determined with five hyperbolic matching criteria and the auto-categorization. This technique is verified with the simulated and measured GPR data about rebars, pipelines, and voids, and results demonstrate that it achieves the average correct rate, average missed rate, and the average misjudged rate is 98.46 %, 1.33%, and 0.36% respectively, and the average correct rate for GPR data of the double-layer rebars is 91.67%. |
|---|---|
| AbstractList | The automatic detection of multiple objects in Ground penetrating radar (GPR) data is investigated by searching for the reflected hyperbolas of buried objects, which can reduce the subjectivity of operators and improve the identification accuracy. Based on Frequency-wavenumber (F-K) migration, the accurate calculation of electromagnetic wave velocity (AC-EWV) is proposed by searching for the minimum image entropy of migrated radargrams. To avoid global searching, potential positions of object hyperbolas are selected from the binarized radargram through the vertical gray gradient searching, then the sub_window is extracted with the potential position as the center. The best fitting hyperbola is detected with the genetic algorithm (GA) in the sub_window, and objects are finally determined with five hyperbolic matching criteria and the auto-categorization. This technique is verified with the simulated and measured GPR data about rebars, pipelines, and voids, and results demonstrate that it achieves the average correct rate, average missed rate, and the average misjudged rate is 98.46 %, 1.33%, and 0.36% respectively, and the average correct rate for GPR data of the double-layer rebars is 91.67%. The automatic detection of multiple objects in ground penetrating radar (GPR) data is investigated by searching for the reflected hyperbolas of buried objects, which can reduce the subjectivity of operators and improve identification accuracy. Based on Frequency-wavenumber (F-K) migration, the accurate calculation of electromagnetic wave velocity (AC-EWV) is proposed by searching for the minimum image entropy of migrated radargrams. To avoid global searching, potential positions of object hyperbolas are selected from the binarized radargram through the vertical gray gradient searching, then the sub_window is extracted with the potential position as the center. The best fitting hyperbola is detected with the genetic algorithm (GA) in the sub_window, and objects are finally determined with five hyperbolic matching criteria and the auto-categorization. This technique is verified with the simulated and measured GPR data about rebars, pipelines, and voids, and results demonstrate that it achieves the average correct rate, average missed rate, and the average misjudged rate is 98.46%, 1.33%, and 0.36%, respectively, and the average correct rate for GPR data of the double-layer rebars is 91.67%. |
| Author | Cui, Guangyan Xu, Jie Zhao, Shengsheng Wang, Yanhui |
| Author_xml | – sequence: 1 givenname: Guangyan orcidid: 0000-0003-2251-2616 surname: Cui fullname: Cui, Guangyan organization: School of Traffic and Transportation, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China – sequence: 2 givenname: Jie surname: Xu fullname: Xu, Jie organization: School of Traffic and Transportation, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China – sequence: 3 givenname: Yanhui orcidid: 0000-0002-2405-7306 surname: Wang fullname: Wang, Yanhui organization: School of Traffic and Transportation, Beijing Research Center of Urban Traffic Information Sensing and Service Technology, Research and Development Center of Transport Industry of Technologies and Equipment of Urban Rail Operation Safety Management, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China – sequence: 4 givenname: Shengsheng surname: Zhao fullname: Zhao, Shengsheng organization: School of Traffic and Transportation, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China |
| BookMark | eNp9kE1Lw0AQhhdRsK3-APGy4Dl1vzc5llKjUFFqVfASdpOJTUmzNbs5-O9NaPHgwdPAy_vMMM8YnTauAYSuKJlSSpLbdbp6mTLC2JQzFktNT9CIShlHRAlxikaEJipiccLO0dj7LSFUSKpH6OOxq0O1rwE7u4U8eGy64HYmVDkuIPRJ5RrsSpw-r3BhgsHWeChwH4YN4Nk8Wry_YdMUOIUGBmpWf7q2CpvdBTorTe3h8jgn6PVusZ7fR8un9GE-W0Y5S3iImAVibSGsTKBQudGGxbzk1pa6EEZzVlJKGS-4kTpPFC81GSYIq6UtheUTdHPYu2_dVwc-ZFvXtU1_MmNaKUUZVaxv6UMrb533LZRZXgUzfBdaU9UZJdkgMhtEZoPI7CiyJ-kfct9WO9N-_8tcH5gKAH77SRIzIRX_AUUogAQ |
| CODEN | IGRSD2 |
| CitedBy_id | crossref_primary_10_1016_j_measurement_2024_115379 crossref_primary_10_1016_j_ndteint_2024_103244 crossref_primary_10_1109_TGRS_2023_3295380 crossref_primary_10_1109_TGRS_2024_3416960 |
| Cites_doi | 10.1016/j.autcon.2020.103106 10.1016/j.ndteint.2018.04.009 10.1109/CADIAG.2017.8075631 10.1016/S0922-3487(03)23001-X 10.1007/s11770-007-0043-6 10.1016/j.jappgeo.2018.09.038 10.1109/3477.662762 10.1016/j.buildenv.2022.109133 10.1016/j.measurement.2020.108330 10.1016/j.tust.2020.103740 10.1016/j.autcon.2016.03.011 10.1109/IGARSS.2008.4779044 10.1016/j.ndteint.2017.06.002 10.1088/2040-8986/abf891 10.1016/j.autcon.2020.103157 10.1016/j.ndteint.2016.05.002 10.1109/JSEN.2021.3050262 10.1016/s0922-3487(03)23002-1 10.1016/j.tranpol.2006.03.002 10.1016/j.aci.2018.10.001 10.1016/j.autcon.2014.05.004 10.1016/j.measurement.2020.107770 10.1109/IGARSS.2009.5417405 10.1016/j.conbuildmat.2017.02.126 10.1016/j.ndteint.2017.03.005 10.1109/IWAGPR.2017.7996100 10.1007/s11220-018-0209-8 10.1109/TGRS.2018.2799586 10.1016/j.chroma.2007.04.025 10.1016/j.ndteint.2017.04.002 10.1016/j.autcon.2018.03.002 10.1155/2014/280738 10.1016/j.asoc.2019.03.030 10.1016/j.autcon.2020.103186 10.1016/j.jappgeo.2019.103856 10.1016/j.autcon.2019.102839 10.1007/s12517-019-4686-4 10.1016/j.autcon.2011.09.010 10.1016/j.autcon.2021.103830 10.1190/1.1440826 10.1016/j.jappgeo.2020.103958 10.1016/j.autcon.2006.09.004 10.1109/TGRS.2003.813497 10.1016/j.ndteint.2017.07.013 10.1016/j.ndteint.2012.03.001 10.1016/j.autcon.2020.103279 10.1016/j.autcon.2018.02.017 10.1016/j.jappgeo.2021.104477 10.3390/electronics9111804 10.1109/TGRS.2009.2012701 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023 |
| DBID | 97E RIA RIE AAYXX CITATION 7UA 8FD C1K F1W FR3 H8D H96 KR7 L.G L7M |
| DOI | 10.1109/TGRS.2022.3228571 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEL CrossRef Water Resources Abstracts Technology Research Database Environmental Sciences and Pollution Management ASFA: Aquatic Sciences and Fisheries Abstracts Engineering Research Database Aerospace Database Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources Civil Engineering Abstracts Aquatic Science & Fisheries Abstracts (ASFA) Professional Advanced Technologies Database with Aerospace |
| DatabaseTitle | CrossRef Aerospace Database Civil Engineering Abstracts Aquatic Science & Fisheries Abstracts (ASFA) Professional Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources Technology Research Database ASFA: Aquatic Sciences and Fisheries Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Water Resources Abstracts Environmental Sciences and Pollution Management |
| DatabaseTitleList | Aerospace Database |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Physics |
| EISSN | 1558-0644 |
| EndPage | 1 |
| ExternalDocumentID | 10_1109_TGRS_2022_3228571 9982456 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: Beijing Jiaotong University grantid: RCS2021ZZ003 funderid: 10.13039/501100005022 |
| GroupedDBID | -~X 0R~ 29I 4.4 5GY 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACGFS ACIWK ACNCT AENEX AFRAH AGQYO AHBIQ AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS F5P HZ~ IFIPE IPLJI JAVBF LAI M43 O9- OCL P2P RIA RIE RNS RXW TAE TN5 Y6R 5VS AAYXX AETIX AGSQL AI. AIBXA CITATION EJD H~9 IBMZZ ICLAB IFJZH VH1 7UA 8FD C1K F1W FR3 H8D H96 KR7 L.G L7M |
| ID | FETCH-LOGICAL-c293t-2be0bbd4b59ed6ca7a283f3bbf7d4a732f11123d3a57c963f707c96e4b75bf4b3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 8 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000917301700002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0196-2892 |
| IngestDate | Mon Jun 30 08:27:23 EDT 2025 Sat Nov 29 02:50:28 EST 2025 Tue Nov 18 21:28:58 EST 2025 Wed Aug 27 02:29:15 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c293t-2be0bbd4b59ed6ca7a283f3bbf7d4a732f11123d3a57c963f707c96e4b75bf4b3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0003-2251-2616 0000-0002-2405-7306 0000-0002-3305-7105 |
| PQID | 2766612162 |
| PQPubID | 85465 |
| PageCount | 1 |
| ParticipantIDs | crossref_citationtrail_10_1109_TGRS_2022_3228571 proquest_journals_2766612162 ieee_primary_9982456 crossref_primary_10_1109_TGRS_2022_3228571 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-01-01 |
| PublicationDateYYYYMMDD | 2023-01-01 |
| PublicationDate_xml | – month: 01 year: 2023 text: 2023-01-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | New York |
| PublicationPlace_xml | – name: New York |
| PublicationTitle | IEEE transactions on geoscience and remote sensing |
| PublicationTitleAbbrev | TGRS |
| PublicationYear | 2023 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref13 ref12 ref15 ref14 ref53 ref52 ref11 ref10 ref17 ref16 ref19 ref18 ref51 ref50 Changfeng (ref44) 2019 ref46 ref45 ref48 ref47 ref42 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref2 ref1 ref39 ref38 Genetic (ref41) 2018; 51 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29 |
| References_xml | – ident: ref19 doi: 10.1016/j.autcon.2020.103106 – ident: ref16 doi: 10.1016/j.ndteint.2018.04.009 – ident: ref27 doi: 10.1109/CADIAG.2017.8075631 – ident: ref50 doi: 10.1016/S0922-3487(03)23001-X – ident: ref49 doi: 10.1007/s11770-007-0043-6 – ident: ref14 doi: 10.1016/j.jappgeo.2018.09.038 – ident: ref46 doi: 10.1109/3477.662762 – ident: ref43 doi: 10.1016/j.buildenv.2022.109133 – ident: ref25 doi: 10.1016/j.measurement.2020.108330 – ident: ref8 doi: 10.1016/j.tust.2020.103740 – ident: ref6 doi: 10.1016/j.autcon.2016.03.011 – ident: ref3 doi: 10.1109/IGARSS.2008.4779044 – ident: ref28 doi: 10.1016/j.ndteint.2017.06.002 – ident: ref47 doi: 10.1088/2040-8986/abf891 – ident: ref10 doi: 10.1016/j.autcon.2020.103157 – ident: ref13 doi: 10.1016/j.ndteint.2016.05.002 – volume: 51 start-page: 187 issue: 10 year: 2018 ident: ref41 article-title: Classifier design by a multi-objective genetic algorithm GPR classifier design by a algorithm GPR classifier design by a approach GPR classifier design by a multi-objective genetic approach publication-title: IFAC-PapersOnLine – ident: ref33 doi: 10.1109/JSEN.2021.3050262 – ident: ref52 doi: 10.1016/s0922-3487(03)23002-1 – ident: ref53 doi: 10.1016/j.tranpol.2006.03.002 – ident: ref4 doi: 10.1016/j.aci.2018.10.001 – ident: ref5 doi: 10.1016/j.autcon.2014.05.004 – ident: ref35 doi: 10.1016/j.measurement.2020.107770 – ident: ref39 doi: 10.1109/IGARSS.2009.5417405 – ident: ref20 doi: 10.1016/j.conbuildmat.2017.02.126 – ident: ref23 doi: 10.1016/j.ndteint.2017.03.005 – ident: ref34 doi: 10.1109/IWAGPR.2017.7996100 – ident: ref17 doi: 10.1007/s11220-018-0209-8 – ident: ref31 doi: 10.1109/TGRS.2018.2799586 – ident: ref51 doi: 10.1016/j.chroma.2007.04.025 – ident: ref26 doi: 10.1016/j.measurement.2020.107770 – ident: ref1 doi: 10.1016/j.ndteint.2017.04.002 – ident: ref9 doi: 10.1016/j.autcon.2018.03.002 – ident: ref15 doi: 10.1155/2014/280738 – start-page: 63 year: 2019 ident: ref44 article-title: Application of GPR to a site investigation involving shallow pipeline and research on GPR signal processing – ident: ref40 doi: 10.1016/j.asoc.2019.03.030 – ident: ref21 doi: 10.1016/j.autcon.2020.103186 – ident: ref30 doi: 10.1016/j.jappgeo.2019.103856 – ident: ref36 doi: 10.1016/j.autcon.2019.102839 – ident: ref48 doi: 10.1007/s12517-019-4686-4 – ident: ref12 doi: 10.1016/j.autcon.2011.09.010 – ident: ref32 doi: 10.1016/j.autcon.2021.103830 – ident: ref42 doi: 10.1190/1.1440826 – ident: ref11 doi: 10.1016/j.jappgeo.2020.103958 – ident: ref7 doi: 10.1016/j.autcon.2006.09.004 – ident: ref45 doi: 10.1109/TGRS.2003.813497 – ident: ref29 doi: 10.1016/j.ndteint.2017.07.013 – ident: ref24 doi: 10.1016/j.ndteint.2012.03.001 – ident: ref22 doi: 10.1016/j.autcon.2020.103279 – ident: ref2 doi: 10.1016/j.autcon.2018.02.017 – ident: ref37 doi: 10.1016/j.jappgeo.2021.104477 – ident: ref18 doi: 10.3390/electronics9111804 – ident: ref38 doi: 10.1109/TGRS.2009.2012701 |
| SSID | ssj0014517 |
| Score | 2.450333 |
| Snippet | The automatic detection of multiple objects in Ground penetrating radar (GPR) data is investigated by searching for the reflected hyperbolas of buried objects,... The automatic detection of multiple objects in ground penetrating radar (GPR) data is investigated by searching for the reflected hyperbolas of buried objects,... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1 |
| SubjectTerms | Accurate Calculation of Electromagnetic Wave Velocity (AC-EWV) Algorithms Detection Electromagnetic radiation Electromagnetic scattering Entropy F-K migration Fitting Genetic algorithm Genetic algorithms GPR Ground penetrating radar Hyperbolas Machine learning algorithms Multiple objects automatic detection Object recognition Pipelines Radar Rebar Search problems Searching Voids Wave velocity Wavelengths |
| Title | Multiple objects automatic detection of GPR data based on the AC-EWV and Genetic Algorithm |
| URI | https://ieeexplore.ieee.org/document/9982456 https://www.proquest.com/docview/2766612162 |
| Volume | 61 |
| WOSCitedRecordID | wos000917301700002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1558-0644 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014517 issn: 0196-2892 databaseCode: RIE dateStart: 19800101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LSwMxEB5UFPTgoypWq-TgSVzdzT7SPRaxelCRWh94WfKYqKC70m79_SZpWhRF8LRhNwNLviTzngHYF3EoQiGioK24DpJIJwFXPDQjZJYhILqQ_7sLdnXVfnjIr2fgcJoLg4gu-AyP7ND58lUlR9ZUdmxUA-unm4VZxrJxrtbUY5CkkU-NzgKjRFDvwYzC_Lh_1rsxmiClR2b3tlMWfeNBrqnKj5vYsZfuyv9-bBWWvRhJOmPc12AGywYsfSku2IAFF9wph-vweOmjBkklrNllSPiorlytVqKwdsFYJak0ObvuERsySixvU8S8NOIh6ZwEp_d3hJeK2CLVlqrz-lQNXurntw247Z72T84D31MhkIax1wEVaKBRiUhzVJnkjBv5QsdCaKYSzmKqzeVHYxXzlElzODUL7RMTwVKhExFvwlxZlbgFRGSoZariTJoPHPO2bRmByggk3IgpGDchnKxyIX3Bcdv34rVwikeYFxaYwgJTeGCacDAleR9X2_hr8rpFYjrRg9CE1gTKwp_HYUGZUdMiGmV0-3eqHVi0jeTHxpUWzNWDEe7CvPyoX4aDPbfVPgH8gNCW |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1bT9swFD5iZRPsgW0wRBljfuBpIpA4Ttw8VojLRKkQlIt4iXw5HkglQW3K78d2TcU0hMRTrMRHivzZPvdzALZkGstYyiTqaGEilhgWCS1iO0LuGAKiD_m_7PF-v3N9XZzOwfYsFwYRffAZ7rih9-XrWk2cqWzXqgbOT_cB5jPGaDzN1pr5DFiWhOToPLJqBA0-zCQudgeHZ-dWF6R0x-7fTsaTf7iQb6vy313sGczBl_f92ldYCoIk6U6R_wZzWC3D5xflBZfhkw_vVOMVuDkJcYOkls7wMiZi0tS-WivR2PhwrIrUhhyenhEXNEocd9PEvrQCIunuRftXl0RUmrgy1Y6qO_xbj-6a2_vvcHGwP9g7ikJXhUhZ1t5EVKIFRzOZFahzJbiwEoZJpTRcM8FTauz1R1OdiowrezwNj90TmeSZNEymq9Cq6grXgMgcjcp0miv7QWDRcU0jUFuRRFhBBdM2xM-rXKpQctx1vhiWXvWIi9IBUzpgygBMG37PSB6m9TbemrzikJhNDCC0YeMZyjKcyHFJuVXUEprkdP11ql-wcDQ46ZW9P_3jH7Do2spPTS0b0GpGE_wJH9Vjczcebfpt9wQAPdPd |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Multiple+Objects+Automatic+Detection+of+GPR+Data+Based+on+the+AC-EWV+and+Genetic+Algorithm&rft.jtitle=IEEE+transactions+on+geoscience+and+remote+sensing&rft.au=Cui%2C+Guangyan&rft.au=Xu%2C+Jie&rft.au=Wang%2C+Yanhui&rft.au=Zhao%2C+Shengsheng&rft.date=2023-01-01&rft.issn=0196-2892&rft.eissn=1558-0644&rft.volume=61&rft.spage=1&rft.epage=16&rft_id=info:doi/10.1109%2FTGRS.2022.3228571&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TGRS_2022_3228571 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0196-2892&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0196-2892&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0196-2892&client=summon |