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3D GPR Image-based UcNet for Enhancing Underground Cavity Detectability
This paper proposes a 3D ground penetrating radar (GPR) image-based underground cavity detection network (UcNet) for preventing sinkholes in complex urban roads. UcNet is developed based on convolutional neural network (CNN) incorporated with phase analysis of super-resolution (SR) GPR images. CNNs...
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| Published in: | Remote sensing (Basel, Switzerland) Switzerland), 2019-11, Vol.11 (21), p.2545 |
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| Main Authors: | , , , , |
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| container_issue | 21 |
| container_start_page | 2545 |
| container_title | Remote sensing (Basel, Switzerland) |
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| creator | Kang, Man-Sung Kim, Namgyu Im, Seok Been Lee, Jong-Jae An, Yun-Kyu |
| description | This paper proposes a 3D ground penetrating radar (GPR) image-based underground cavity detection network (UcNet) for preventing sinkholes in complex urban roads. UcNet is developed based on convolutional neural network (CNN) incorporated with phase analysis of super-resolution (SR) GPR images. CNNs have been popularly used for automated GPR data classification, because expert-dependent data interpretation of massive GPR data obtained from urban roads is typically cumbersome and time consuming. However, the conventional CNNs often provide misclassification results due to similar GPR features automatically extracted from arbitrary underground objects such as cavities, manholes, gravels, subsoil backgrounds and so on. In particular, non-cavity features are often misclassified as real cavities, which degrades the CNNs’ performance and reliability. UcNet improves underground cavity detectability by generating SR GPR images of the cavities extracted from CNN and analyzing their phase information. The proposed UcNet is experimentally validated using in-situ GPR data collected from complex urban roads in Seoul, South Korea. The validation test results reveal that the underground cavity misclassification is remarkably decreased compared to the conventional CNN ones. |
| doi_str_mv | 10.3390/rs11212545 |
| format | article |
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UcNet is developed based on convolutional neural network (CNN) incorporated with phase analysis of super-resolution (SR) GPR images. CNNs have been popularly used for automated GPR data classification, because expert-dependent data interpretation of massive GPR data obtained from urban roads is typically cumbersome and time consuming. However, the conventional CNNs often provide misclassification results due to similar GPR features automatically extracted from arbitrary underground objects such as cavities, manholes, gravels, subsoil backgrounds and so on. In particular, non-cavity features are often misclassified as real cavities, which degrades the CNNs’ performance and reliability. UcNet improves underground cavity detectability by generating SR GPR images of the cavities extracted from CNN and analyzing their phase information. The proposed UcNet is experimentally validated using in-situ GPR data collected from complex urban roads in Seoul, South Korea. The validation test results reveal that the underground cavity misclassification is remarkably decreased compared to the conventional CNN ones.</description><identifier>ISSN: 2072-4292</identifier><identifier>EISSN: 2072-4292</identifier><identifier>DOI: 10.3390/rs11212545</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Artificial neural networks ; automated underground object classification ; Automation ; Cavities ; Classification ; Data interpretation ; deep convolutional neural network ; False alarms ; Feature extraction ; Ground penetrating radar ; Holes ; Image classification ; Image enhancement ; Image resolution ; Manholes ; Neural networks ; Object recognition ; phase analysis ; Radar imaging ; Remote sensing ; Roads ; Sinkholes ; Subsoils ; super-resolution ; underground cavity detection network ; Underground roadways ; Urban areas</subject><ispartof>Remote sensing (Basel, Switzerland), 2019-11, Vol.11 (21), p.2545</ispartof><rights>2019 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a384t-c7f78c1697fe688b0a80866d9a7866b17bd23a9dbccf34fd04e3841515aaee953</citedby><cites>FETCH-LOGICAL-a384t-c7f78c1697fe688b0a80866d9a7866b17bd23a9dbccf34fd04e3841515aaee953</cites><orcidid>0000-0003-3852-9025</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2550280607/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2550280607?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25752,27923,27924,37011,44589,74997</link.rule.ids></links><search><creatorcontrib>Kang, Man-Sung</creatorcontrib><creatorcontrib>Kim, Namgyu</creatorcontrib><creatorcontrib>Im, Seok Been</creatorcontrib><creatorcontrib>Lee, Jong-Jae</creatorcontrib><creatorcontrib>An, Yun-Kyu</creatorcontrib><title>3D GPR Image-based UcNet for Enhancing Underground Cavity Detectability</title><title>Remote sensing (Basel, Switzerland)</title><description>This paper proposes a 3D ground penetrating radar (GPR) image-based underground cavity detection network (UcNet) for preventing sinkholes in complex urban roads. UcNet is developed based on convolutional neural network (CNN) incorporated with phase analysis of super-resolution (SR) GPR images. CNNs have been popularly used for automated GPR data classification, because expert-dependent data interpretation of massive GPR data obtained from urban roads is typically cumbersome and time consuming. However, the conventional CNNs often provide misclassification results due to similar GPR features automatically extracted from arbitrary underground objects such as cavities, manholes, gravels, subsoil backgrounds and so on. In particular, non-cavity features are often misclassified as real cavities, which degrades the CNNs’ performance and reliability. UcNet improves underground cavity detectability by generating SR GPR images of the cavities extracted from CNN and analyzing their phase information. The proposed UcNet is experimentally validated using in-situ GPR data collected from complex urban roads in Seoul, South Korea. The validation test results reveal that the underground cavity misclassification is remarkably decreased compared to the conventional CNN ones.</description><subject>Artificial neural networks</subject><subject>automated underground object classification</subject><subject>Automation</subject><subject>Cavities</subject><subject>Classification</subject><subject>Data interpretation</subject><subject>deep convolutional neural network</subject><subject>False alarms</subject><subject>Feature extraction</subject><subject>Ground penetrating radar</subject><subject>Holes</subject><subject>Image classification</subject><subject>Image enhancement</subject><subject>Image resolution</subject><subject>Manholes</subject><subject>Neural networks</subject><subject>Object recognition</subject><subject>phase analysis</subject><subject>Radar imaging</subject><subject>Remote sensing</subject><subject>Roads</subject><subject>Sinkholes</subject><subject>Subsoils</subject><subject>super-resolution</subject><subject>underground cavity detection network</subject><subject>Underground roadways</subject><subject>Urban areas</subject><issn>2072-4292</issn><issn>2072-4292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpNkE1LAzEQhoMoWGov_oKAN2F18rWbHKWttVBUxJ7DJJutW9rdmmyF_ntXK-pc3pnhnWeGIeSSwY0QBm5jYowzrqQ6IQMOBc8kN_z0X35ORimtoQ8hmAE5IDMxobPnFzrf4ipkDlMo6dI_ho5WbaTT5g0bXzcrumzKEFex3TclHeNH3R3oJHTBd-jqTV9dkLMKNymMfnRIlvfT1_FDtniazcd3iwyFll3mi6rQnuWmqEKutQPUoPO8NFj04ljhSi7QlM77SsiqBBn6OaaYQgzBKDEk8yO3bHFtd7HeYjzYFmv73WjjymLsar8JFrQzHoAXzoN0II3jvIcwVBxAmdCzro6sXWzf9yF1dt3uY9Ofb7lSwDXkUPSu66PLxzalGKrfrQzs19_t39_FJ8Bjcno</recordid><startdate>20191101</startdate><enddate>20191101</enddate><creator>Kang, Man-Sung</creator><creator>Kim, 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GPR Image-based UcNet for Enhancing Underground Cavity Detectability</title><author>Kang, Man-Sung ; Kim, Namgyu ; Im, Seok Been ; Lee, Jong-Jae ; An, Yun-Kyu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a384t-c7f78c1697fe688b0a80866d9a7866b17bd23a9dbccf34fd04e3841515aaee953</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Artificial neural networks</topic><topic>automated underground object classification</topic><topic>Automation</topic><topic>Cavities</topic><topic>Classification</topic><topic>Data interpretation</topic><topic>deep convolutional neural network</topic><topic>False alarms</topic><topic>Feature extraction</topic><topic>Ground penetrating radar</topic><topic>Holes</topic><topic>Image classification</topic><topic>Image enhancement</topic><topic>Image resolution</topic><topic>Manholes</topic><topic>Neural networks</topic><topic>Object 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UcNet is developed based on convolutional neural network (CNN) incorporated with phase analysis of super-resolution (SR) GPR images. CNNs have been popularly used for automated GPR data classification, because expert-dependent data interpretation of massive GPR data obtained from urban roads is typically cumbersome and time consuming. However, the conventional CNNs often provide misclassification results due to similar GPR features automatically extracted from arbitrary underground objects such as cavities, manholes, gravels, subsoil backgrounds and so on. In particular, non-cavity features are often misclassified as real cavities, which degrades the CNNs’ performance and reliability. UcNet improves underground cavity detectability by generating SR GPR images of the cavities extracted from CNN and analyzing their phase information. The proposed UcNet is experimentally validated using in-situ GPR data collected from complex urban roads in Seoul, South Korea. The validation test results reveal that the underground cavity misclassification is remarkably decreased compared to the conventional CNN ones.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/rs11212545</doi><orcidid>https://orcid.org/0000-0003-3852-9025</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Artificial neural networks automated underground object classification Automation Cavities Classification Data interpretation deep convolutional neural network False alarms Feature extraction Ground penetrating radar Holes Image classification Image enhancement Image resolution Manholes Neural networks Object recognition phase analysis Radar imaging Remote sensing Roads Sinkholes Subsoils super-resolution underground cavity detection network Underground roadways Urban areas |
| title | 3D GPR Image-based UcNet for Enhancing Underground Cavity Detectability |
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