High-Precision Inversion of Shallow Bathymetry under Complex Hydrographic Conditions Using VGG19—A Case Study of the Taiwan Banks
Shallow bathymetry is important for ocean exploration, and the development of high-precision bathymetry inversion methods, especially for shallow waters with poor quality, is a major research aim. Synthetic aperture radar (SAR) image data benefit from a wide coverage, high measurement density, rapid...
Saved in:
Published in | Remote sensing (Basel, Switzerland) Vol. 15; no. 5; p. 1257 |
---|---|
Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
Basel
MDPI AG
01.03.2023
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Shallow bathymetry is important for ocean exploration, and the development of high-precision bathymetry inversion methods, especially for shallow waters with poor quality, is a major research aim. Synthetic aperture radar (SAR) image data benefit from a wide coverage, high measurement density, rapidity, and low consumption but are limited by low accuracy. Alternatively, multibeam data have low coverage and are difficult to obtain but have a high measurement accuracy. In this paper, taking advantage of the complementary properties, we use SAR image data as the content map and multibeam images as the migrated style map, applying the VGG19 neural network (optimizing the loss function formula) for bathymetric inversion. The model was universal and highly accurate for bathymetric inversion of shallow marine areas, such as turbid water in Taiwan. There was a strong correlation between bathymetric inversion data and measured data (R2 = 0.8822; RMSE = 1.86 m). The relative error was refined by 9.22% over those of previous studies. Values for different bathymetric regions were extremely correlated in the region of 20–40 m. The newly developed approach is highly accurate over 20 m in the open ocean, providing an efficient, precise shallow bathymetry inversion method for complex hydrographic conditions. |
---|---|
AbstractList | Shallow bathymetry is important for ocean exploration, and the development of high-precision bathymetry inversion methods, especially for shallow waters with poor quality, is a major research aim. Synthetic aperture radar (SAR) image data benefit from a wide coverage, high measurement density, rapidity, and low consumption but are limited by low accuracy. Alternatively, multibeam data have low coverage and are difficult to obtain but have a high measurement accuracy. In this paper, taking advantage of the complementary properties, we use SAR image data as the content map and multibeam images as the migrated style map, applying the VGG19 neural network (optimizing the loss function formula) for bathymetric inversion. The model was universal and highly accurate for bathymetric inversion of shallow marine areas, such as turbid water in Taiwan. There was a strong correlation between bathymetric inversion data and measured data (R[sup.2] = 0.8822; RMSE = 1.86 m). The relative error was refined by 9.22% over those of previous studies. Values for different bathymetric regions were extremely correlated in the region of 20–40 m. The newly developed approach is highly accurate over 20 m in the open ocean, providing an efficient, precise shallow bathymetry inversion method for complex hydrographic conditions. Shallow bathymetry is important for ocean exploration, and the development of high-precision bathymetry inversion methods, especially for shallow waters with poor quality, is a major research aim. Synthetic aperture radar (SAR) image data benefit from a wide coverage, high measurement density, rapidity, and low consumption but are limited by low accuracy. Alternatively, multibeam data have low coverage and are difficult to obtain but have a high measurement accuracy. In this paper, taking advantage of the complementary properties, we use SAR image data as the content map and multibeam images as the migrated style map, applying the VGG19 neural network (optimizing the loss function formula) for bathymetric inversion. The model was universal and highly accurate for bathymetric inversion of shallow marine areas, such as turbid water in Taiwan. There was a strong correlation between bathymetric inversion data and measured data (R2 = 0.8822; RMSE = 1.86 m). The relative error was refined by 9.22% over those of previous studies. Values for different bathymetric regions were extremely correlated in the region of 20–40 m. The newly developed approach is highly accurate over 20 m in the open ocean, providing an efficient, precise shallow bathymetry inversion method for complex hydrographic conditions. |
Audience | Academic |
Author | Qin, Xiaoming Cui, Jiaxin Wu, Ziyin Wan, Hongyang Zhou, Jieqiong Chen, Xiaolun Luo, Xiaowen |
Author_xml | – sequence: 1 givenname: Jiaxin surname: Cui fullname: Cui, Jiaxin – sequence: 2 givenname: Xiaowen surname: Luo fullname: Luo, Xiaowen – sequence: 3 givenname: Ziyin surname: Wu fullname: Wu, Ziyin – sequence: 4 givenname: Jieqiong surname: Zhou fullname: Zhou, Jieqiong – sequence: 5 givenname: Hongyang surname: Wan fullname: Wan, Hongyang – sequence: 6 givenname: Xiaolun surname: Chen fullname: Chen, Xiaolun – sequence: 7 givenname: Xiaoming orcidid: 0000-0001-8408-3998 surname: Qin fullname: Qin, Xiaoming |
BookMark | eNpNkd2KFDEQhRtZwXXdG58g4J3Qa367O5fjoDMDCwq7621I56c7Y08yJhnXvhN8BZ_QJzEzI2rVRRWHqo8D53l14YM3VfUSwRtCOHwTE2KQIczaJ9Ulhi2uKeb44r_9WXWd0haWIgRxSC-rH2s3jPXHaJRLLniw8V9NPG3BgrtRTlN4BG9lHuedyXEGB69NBMuw20_mG1jPOoYhyv3oVBG9drm8JvCQnB_Ap9UK8V_ffy7AUiYD7vJBz0dsHg24l-5R-kL2n9OL6qmVUzLXf-ZV9fD-3f1yXd9-WG2Wi9taEcZzTSnDlsFWGt1BxDvddZR1jVWEGNsT3EFFMekb3DUQ99BYC5WiBjFKG8W4JlfV5szVQW7FPrqdjLMI0omTEOIgZMxOTUagHiEsdQslUtRSzQtX4wZZia1kPS-sV2fWPoYvB5Oy2IZD9MW-wG3HMGlaSsrVzflqkAXqvA05SlVam51TJT3rir5oT7E18Ih9fX5QMaQUjf1rE0FxDFn8C5n8Bt4gmyE |
Cites_doi | 10.1109/CVPR.2016.265 10.1109/TGRS.2020.3015157 10.1109/TGRS.2006.872909 10.1117/12.466156 10.1109/CVPR.2014.81 10.1109/TGRS.2021.3081691 10.1080/01431160116928 10.1364/AO.17.000379 10.1007/s00190-022-01693-y 10.1016/j.oceaneng.2019.04.084 10.18307/2008.0515 10.1109/TGRS.2019.2907932 10.3390/rs14235939 10.1109/TGRS.2016.2636241 10.1080/01431168508948428 10.1515/geo-2020-0267 10.1080/01431169208904242 10.1109/LGRS.2015.2496401 10.1504/IJCSYSE.2019.098418 10.1167/16.12.326 |
ContentType | Journal Article |
Copyright | COPYRIGHT 2023 MDPI AG 2023 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 (https://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. |
Copyright_xml | – notice: COPYRIGHT 2023 MDPI AG – notice: 2023 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 (https://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. |
DBID | AAYXX CITATION 7QF 7QO 7QQ 7QR 7SC 7SE 7SN 7SP 7SR 7TA 7TB 7U5 8BQ 8FD 8FE 8FG ABJCF ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ BHPHI BKSAR C1K CCPQU DWQXO F28 FR3 H8D H8G HCIFZ JG9 JQ2 KR7 L6V L7M L~C L~D M7S P5Z P62 P64 PCBAR PIMPY PQEST PQQKQ PQUKI PTHSS DOA |
DOI | 10.3390/rs15051257 |
DatabaseName | CrossRef Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts Chemoreception Abstracts Computer and Information Systems Abstracts Corrosion Abstracts Ecology Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Central Technology Collection ProQuest Natural Science Collection Earth, Atmospheric & Aquatic Science Collection Environmental Sciences and Pollution Management ProQuest One Community College ProQuest Central ANTE: Abstracts in New Technology & Engineering Engineering Research Database Aerospace Database Copper Technical Reference Library SciTech Premium Collection Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts ProQuest Engineering Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Engineering Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Earth, Atmospheric & Aquatic Science Database Publicly Available Content Database ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition Engineering Collection DOAJ Directory of Open Access Journals |
DatabaseTitle | CrossRef Publicly Available Content Database Materials Research Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection Materials Business File Environmental Sciences and Pollution Management Engineered Materials Abstracts Natural Science Collection Chemoreception Abstracts Engineering Collection ANTE: Abstracts in New Technology & Engineering Advanced Technologies & Aerospace Collection Engineering Database Aluminium Industry Abstracts ProQuest One Academic Eastern Edition Electronics & Communications Abstracts Earth, Atmospheric & Aquatic Science Database ProQuest Technology Collection Ceramic Abstracts Ecology Abstracts Biotechnology and BioEngineering Abstracts ProQuest One Academic UKI Edition Solid State and Superconductivity Abstracts Engineering Research Database ProQuest One Academic Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts ProQuest Central (Alumni Edition) ProQuest One Community College Earth, Atmospheric & Aquatic Science Collection ProQuest Central Aerospace Database Copper Technical Reference Library ProQuest Engineering Collection Biotechnology Research Abstracts ProQuest Central Korea Advanced Technologies Database with Aerospace Civil Engineering Abstracts ProQuest SciTech Collection METADEX Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database Materials Science & Engineering Collection Corrosion Abstracts |
DatabaseTitleList | Publicly Available Content Database CrossRef |
Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Geography |
EISSN | 2072-4292 |
ExternalDocumentID | oai_doaj_org_article_1b112ad70a1c4f4d93b6d261fa2fa5b9 A750512609 10_3390_rs15051257 |
GeographicLocations | China Taiwan |
GeographicLocations_xml | – name: China – name: Taiwan |
GroupedDBID | 29P 2WC 2XV 5VS 8FE 8FG 8FH AADQD AAHBH AAYXX ABDBF ABJCF ADBBV AENEX AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS ARAPS BCNDV BENPR BGLVJ BHPHI BKSAR CCPQU CITATION E3Z ESX FRP GROUPED_DOAJ HCIFZ I-F IAO ITC KQ8 L6V LK5 M7R M7S MODMG M~E OK1 P62 PCBAR PIMPY PROAC PTHSS RIG TR2 TUS 7QF 7QO 7QQ 7QR 7SC 7SE 7SN 7SP 7SR 7TA 7TB 7U5 8BQ 8FD ABUWG AZQEC C1K DWQXO F28 FR3 H8D H8G JG9 JQ2 KR7 L7M L~C L~D P64 PQEST PQQKQ PQUKI |
ID | FETCH-LOGICAL-c359t-4452f507aed80198d884586fc33efb3280c423b628602b0eff0cc4e15446c59d3 |
IEDL.DBID | 8FG |
ISSN | 2072-4292 |
IngestDate | Fri Oct 04 13:00:45 EDT 2024 Thu Oct 10 19:51:29 EDT 2024 Tue Sep 03 04:00:14 EDT 2024 Thu Sep 26 16:48:40 EDT 2024 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 5 |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c359t-4452f507aed80198d884586fc33efb3280c423b628602b0eff0cc4e15446c59d3 |
ORCID | 0000-0001-8408-3998 |
OpenAccessLink | https://www.proquest.com/docview/2785236743?pq-origsite=%requestingapplication% |
PQID | 2785236743 |
PQPubID | 2032338 |
ParticipantIDs | doaj_primary_oai_doaj_org_article_1b112ad70a1c4f4d93b6d261fa2fa5b9 proquest_journals_2785236743 gale_infotracacademiconefile_A750512609 crossref_primary_10_3390_rs15051257 |
PublicationCentury | 2000 |
PublicationDate | 2023-03-01 |
PublicationDateYYYYMMDD | 2023-03-01 |
PublicationDate_xml | – month: 03 year: 2023 text: 2023-03-01 day: 01 |
PublicationDecade | 2020 |
PublicationPlace | Basel |
PublicationPlace_xml | – name: Basel |
PublicationTitle | Remote sensing (Basel, Switzerland) |
PublicationYear | 2023 |
Publisher | MDPI AG |
Publisher_xml | – name: MDPI AG |
References | Zhang (ref_40) 2022; 96 Chen (ref_9) 2012; 51 Xu (ref_11) 2022; 38 Zhao (ref_15) 2022; 41 ref_36 Calkoen (ref_22) 2001; 22 ref_35 ref_30 Li (ref_37) 2003; 4892 Zhang (ref_6) 1998; 6 Ruiyao (ref_18) 2022; 7 ref_16 Gatys (ref_33) 2015; 16 ref_38 Shen (ref_17) 2022; 37 Shi (ref_42) 2019; 183 Lyzenga (ref_4) 1985; 6 Lu (ref_12) 2022; 44 Hong (ref_29) 2021; 59 Wang (ref_10) 2022; 42 Vogelzang (ref_21) 1992; 13 Figueiredo (ref_8) 2016; 13 Pan (ref_32) 2021; 41 Fan (ref_25) 2012; 55 Lyzenga (ref_3) 1978; 17 Gupta (ref_34) 2019; 5 Zhai (ref_20) 2012; 32 Zhang (ref_41) 2022; 95 ref_24 ref_23 Tian (ref_13) 2015; 34 Ye (ref_2) 2007; 29 Zhu (ref_14) 2021; 41 Mou (ref_27) 2017; 55 Zhao (ref_19) 2021; 13 Shen (ref_39) 2019; 2 ref_26 ref_5 Leifeng (ref_1) 2008; 20 Lyzenga (ref_7) 2006; 44 Evans (ref_31) 2022; 60 Li (ref_28) 2019; 57 |
References_xml | – volume: 32 start-page: 67 year: 2012 ident: ref_20 article-title: Research progress of airborne laser bathymetry technology publication-title: Mar. Surv. Mapp. contributor: fullname: Zhai – volume: 42 start-page: 87 year: 2022 ident: ref_10 article-title: An uncontrolled bathymetric inversion method based on an adaptive empirical semi-analytical model for the South China Sea publication-title: J. Optics. contributor: fullname: Wang – ident: ref_5 – ident: ref_24 – volume: 7 start-page: 33 year: 2022 ident: ref_18 article-title: Application of Catboost model in water depth inversion publication-title: Bull. Surv. Mapp. contributor: fullname: Ruiyao – ident: ref_26 – volume: 41 start-page: 1 year: 2022 ident: ref_15 article-title: Remote sensing bathymetry inversion by neural network based on elastic gradient descent model publication-title: J. Shandong Univ. Sci. Technol. contributor: fullname: Zhao – ident: ref_36 doi: 10.1109/CVPR.2016.265 – volume: 37 start-page: 14 year: 2022 ident: ref_17 article-title: Remote sensing bathymetry inversion of shallow sea based on “grid search + XGBoost” algorithm publication-title: Remote Sens. Inform. contributor: fullname: Shen – volume: 59 start-page: 5966 year: 2021 ident: ref_29 article-title: Graph convolutional networks for hyperspectral image classification publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2020.3015157 contributor: fullname: Hong – volume: 34 start-page: 1 year: 2015 ident: ref_13 article-title: Research on active and passive remote inversion of bathymetry based on Landsat-8 remote sensing images and LiDAR bathymetry data publication-title: J. Mar. Technol. contributor: fullname: Tian – ident: ref_16 – volume: 41 start-page: 142 year: 2021 ident: ref_32 article-title: Bi-LSTM-based depth inversion of near-shore water bodies publication-title: J. Opt. contributor: fullname: Pan – volume: 44 start-page: 2251 year: 2006 ident: ref_7 article-title: Multispectral bathymetry using a simple physically based algorithm publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2006.872909 contributor: fullname: Lyzenga – volume: 4892 start-page: 382 year: 2003 ident: ref_37 article-title: Optical image modulation above the submarine bottom topography: A case study on the Taiwan Banks, China publication-title: Ocean Remote Sens. Appl. doi: 10.1117/12.466156 contributor: fullname: Li – ident: ref_23 – ident: ref_30 doi: 10.1109/CVPR.2014.81 – volume: 60 start-page: 4202709 year: 2022 ident: ref_31 article-title: Toward the detection and imaging of ocean microplastics with a spaceborne radar publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2021.3081691 contributor: fullname: Evans – volume: 22 start-page: 2973 year: 2001 ident: ref_22 article-title: The Bathymetry Assessment System: Efficient depth mapping in shallow seas using radar images publication-title: Int. J. Remote Sens. doi: 10.1080/01431160116928 contributor: fullname: Calkoen – volume: 55 start-page: 310 year: 2012 ident: ref_25 article-title: Example study of SAR remote sensing detection of shallow water depth in shallow Taiwan publication-title: J. Geophys. contributor: fullname: Fan – volume: 17 start-page: 379 year: 1978 ident: ref_3 article-title: Passive remote sensing techniques for mapping water depth and bottom features publication-title: Appl. Opt. doi: 10.1364/AO.17.000379 contributor: fullname: Lyzenga – volume: 96 start-page: 1 year: 2022 ident: ref_40 article-title: PPP-RTK: From common-view to all-in-view GNSS networks publication-title: J. Geod. doi: 10.1007/s00190-022-01693-y contributor: fullname: Zhang – volume: 183 start-page: 224 year: 2019 ident: ref_42 article-title: A 39-year high resolution wave hindcast for the Chinese coast: Model validation and wave climate analysis publication-title: Ocean Eng. doi: 10.1016/j.oceaneng.2019.04.084 contributor: fullname: Shi – volume: 20 start-page: 655 year: 2008 ident: ref_1 article-title: Remote mantle inversion model of water depth based on sediment factor publication-title: Lake Sci. doi: 10.18307/2008.0515 contributor: fullname: Leifeng – volume: 57 start-page: 6690 year: 2019 ident: ref_28 article-title: Deep learning for hyperspectral image classification: An overview publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2019.2907932 contributor: fullname: Li – volume: 44 start-page: 134 year: 2022 ident: ref_12 article-title: Comparative study of shallow sea bathymetry inversion based on GeoEye-1 and WorldView-2 remote sensing data publication-title: J. Oceanogr. contributor: fullname: Lu – ident: ref_35 doi: 10.3390/rs14235939 – volume: 95 start-page: 1 year: 2022 ident: ref_41 article-title: Integer-estimable FDMA Model as an Enabler of GLONASS PPP-RTK publication-title: J. Geod. contributor: fullname: Zhang – volume: 29 start-page: 76 year: 2007 ident: ref_2 article-title: Multispectral water depth remote sensing methods and research progress publication-title: World Sci. Technol. Res. Dev. contributor: fullname: Ye – volume: 55 start-page: 3639 year: 2017 ident: ref_27 article-title: Deep recurrent neural networks for hyperspectral image classification publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2016.2636241 contributor: fullname: Mou – volume: 41 start-page: 42 year: 2021 ident: ref_14 article-title: An improved water depth inversion method for geo-weighted regression model publication-title: Mar. Mapp. contributor: fullname: Zhu – volume: 6 start-page: 115 year: 1985 ident: ref_4 article-title: Shallow-water bathymetry using combined lidar and passive multispectral scanner data publication-title: Int. J. Remote Sens. doi: 10.1080/01431168508948428 contributor: fullname: Lyzenga – volume: 38 start-page: 53 year: 2022 ident: ref_11 article-title: Water depth inversion of coal mining subsidence waters based on multispectral remote sensing and SPXY publication-title: J. Chifeng Coll. contributor: fullname: Xu – volume: 13 start-page: 782 year: 2021 ident: ref_19 article-title: Water deep mapping from Hj-1b satellite data by a deep network model in the sea area of Pearl River Estuary, China publication-title: Open Geosci. doi: 10.1515/geo-2020-0267 contributor: fullname: Zhao – ident: ref_38 – volume: 6 start-page: 95 year: 1998 ident: ref_6 article-title: The establishment of remote sensing model for statistical correlation of water depth publication-title: J. River Sea Univ. contributor: fullname: Zhang – volume: 13 start-page: 1943 year: 1992 ident: ref_21 article-title: Sea bottom topography with X-band SLAR:the relation between radar imagery and bathymetry publication-title: Int. J. Remote Sens. doi: 10.1080/01431169208904242 contributor: fullname: Vogelzang – volume: 13 start-page: 53 year: 2016 ident: ref_8 article-title: A modified Lyzenga′s model for multispectral bathymetry using Tikhonov regularization publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2015.2496401 contributor: fullname: Figueiredo – volume: 5 start-page: 53 year: 2019 ident: ref_34 article-title: Image style transfer using convolutional neural networks based on transfer learning publication-title: Int. J. Comput. Syst. Eng. doi: 10.1504/IJCSYSE.2019.098418 contributor: fullname: Gupta – volume: 2 start-page: 184 year: 2019 ident: ref_39 article-title: Simulation analysis of remote sensing inversion of wave wavelength and water depth based on fast Fourier transform method publication-title: J. East China Norm. Univ. Natur. Sci. Ed. contributor: fullname: Shen – volume: 16 start-page: 326 year: 2015 ident: ref_33 article-title: A neural algorithm of artistic style publication-title: J. Vis. doi: 10.1167/16.12.326 contributor: fullname: Gatys – volume: 51 start-page: 122 year: 2012 ident: ref_9 article-title: Remote sensing of water depth in Guangdong Feilaixia reservoir publication-title: J. Zhongshan Univ. contributor: fullname: Chen |
SSID | ssj0000331904 |
Score | 2.3777885 |
Snippet | Shallow bathymetry is important for ocean exploration, and the development of high-precision bathymetry inversion methods, especially for shallow waters with... |
SourceID | doaj proquest gale crossref |
SourceType | Open Website Aggregation Database |
StartPage | 1257 |
SubjectTerms | Accuracy Algorithms Artificial intelligence Back propagation Bathymeters Bathymetry Deep learning Inversion Machine learning Marine environment Mean square errors Measurement techniques Methods multibeam sonar data Neural networks Propagation Radar imaging Remote sensing Root-mean-square errors Satellites shallow bathymetry inversion Shallow water Simulation Synthetic aperture radar synthetic aperture radar image data Topography Underwater exploration VGG model |
SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1NSx0xFA3FTbsR2yo-ayXQgqvBTD5mJsunqI9CS6Eq7kKSSVDUeWXeFDu7Qv-Cv9Bf0nsnY30bceM2hCTkJLnnztx7LiGfLUdhJ3j9XNQxk06yzDEHXkqoal3anCe5pq_fitmp_HKuzpdKfWFMWJIHThu3lztgBLYumc29jLLWwhU10P5oebTKpdS9XC05U8MbLOBoMZn0SAX49XvtAqgPWDe0Q0sWaBDqf-o5HmzM0RpZHckhnaZFvSWvQvOOvB7rlF_078lfDMvIvrdjYRyKKhnD9y46j_QH1kWZ39J9IHX9TejanmKGWEvxzl-H33TW120a6tJDI_6sxkNHh7ABenZ8nOv7P3dTegCWjWKAYY_DAkOkJ_by1jYwcnO1WCenR4cnB7NsLKOQeaF0l0mpeATaZ0MN5khXdVVJVRXRCxGiE7xiHjiVwxxVxh0LMTLvZUCZnsIrXYsNstLMm7BJaOkYtwVSrFLJ0kUrogpK8OiYijrXE_LpYWvNz6SWYcDLQADMIwATso-7_r8HKlwPDYC7GXE3z-E-IbuImcF72LXW2zGdABaKilZmWg6zFQx6bj_AasYLujC8rBSK10mx9RKr-UDeYB36FJy2TVa69lf4CGylczvDwfwHoHroVw priority: 102 providerName: Directory of Open Access Journals |
Title | High-Precision Inversion of Shallow Bathymetry under Complex Hydrographic Conditions Using VGG19—A Case Study of the Taiwan Banks |
URI | https://www.proquest.com/docview/2785236743 https://doaj.org/article/1b112ad70a1c4f4d93b6d261fa2fa5b9 |
Volume | 15 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwELagPcAF8RSlZWUJJE5RHT_yOKHdqrsrJKoKWtSbZTs2VLRJmw0quVXqX-AX8kuYSbwtF7g6kW15xp5v7JlvCHlrOBI7welnQxkSaSVLLLPgpfiiKnOT8pGu6eNBtjyWH07USbxwW8WwyvWZOBzUVePwjnyX54VCtjEp3l9cJlg1Cl9XYwmN-2QzRSY8zBSfL27vWJgABWNyZCUV4N3vtisAQGDj0Br9ZYcGuv5_HcqDpZk_Jo8iRKTTUaZPyD1fPyUPYrXyb_0zcoPBGclhG8vjUOTKGG69aBPoZ6yO0lzRGUC7_tx3bU8xT6yluPPP_E-67Kt27OrUQSM-WaPq0SF4gH5ZLNLy9_WvKd0D-0YxzLDHbgEn0iNzemVq6Ln-vnpOjuf7R3vLJBZTSJxQZZdIqXgA8Gd8BUapLKqikKrIghPCByt4wRwgK4uZqoxb5kNgzkmPZD2ZU2UlXpCNuqn9S0Jzy7jJEGjlSuY2GBGUV4IHy1Qo03KLvFkvrb4YOTM0-BooAH0ngC0yw1W__QN5roeGpv2q47bRqQU8aKqcmdTJIKsSZliB0xcMD0ZZGOodykzjbuxa40xMKoCJIq-VnubDaBmDP3fWYtVxm670nVK9-v_nbfIQ68yPwWc7ZKNrf_jXgEY6OxlUbkI2Z_sHh58mg0__Bwrc4cc |
link.rule.ids | 315,786,790,870,2115,12792,21416,27955,27956,33406,33777,43633,43838,74390,74657 |
linkProvider | ProQuest |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwELZgeygXxFMUClgCiVNUJ7bzOKHdqu0C7aqCLerN8hMqICnZoJIbEn-BX8gvYSbxtlzg6kS25Rl7vhmPvyHkuc6Q2AlOPxOqkAgjWGKYAS_Fl64qdJqNdE1Hi3x-Il6fytMYcFvFtMr1mTgc1K6xGCPfyYpSItuY4C_PvyZYNQpvV2MJjetkAyk3ywnZmO0tjt9eRlkYBxVjYuQl5eDf77QrgEBg5dAe_WWJBsL-fx3Lg63Zv0VuRpBIp6NUb5Nrvr5DNmO98o_9XfIT0zOS4zYWyKHIljHEvWgT6Dusj9Jc0BmAu_6L79qe4kuxluLe_-y_03nv2rGrMwuNeGmNykeH9AH6_uAgrX7_-DWlu2DhKCYa9tgtIEW61GcXuoae60-re-Rkf2-5O09iOYXEcll1iRAyCwD_tHdglqrSlaWQZR4s5z4YnpXMArYy-FaVZYb5EJi1wiNdT25l5fh9Mqmb2j8gtDAs0zlCrUKKwgTNg_SSZ8EwGaq02iLP1kurzkfWDAXeBgpAXQlgi8xw1S__QKbroaFpP6i4cVRqABFqVzCdWhGEq2CGDty-oLOgpYGhXqDMFO7HrtVWx2cFMFFktlLTYhgtZ_Dn9lqsKm7UlbpSq4f___yUbM6XR4fq8NXizSNyA6vOj6lo22TStd_8Y8AmnXkSFfAPPETjjw |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwELagSNAL4ilKC1gCiVO0jh95nNC2ZXd5VZVoUW-W7dhQUZI2G1RyQ-Iv8Av5JZ1JvC0XuNqWbXnGM5_t8TeEvDAciZ3A-tlQhkRayRLLLJxSfFGVuUn5SNf0YS9bHMq3R-ooxj8tY1jlyiYOhrpqHN6RT3heKGQbk2ISYljE_u7s1elZghmk8KU1ptO4Tm4gyMY0DsVsfnnfwgQoG5MjQ6mA-km7BDAE_g49018-aaDu_5eBHrzO7A65HeEinY7yvUuu-foeuRUzl3_p75NfGKiR7LcxVQ5F3ozhBow2gX7ETCnNOd0GmNd_813bU_wz1lK0Aif-B130VTt2deygEJ-vUQ3pEEhAP83nafnn5-8p3QFfRzHksMduATPSA3N8bmrouf66fEAOZ68PdhZJTKyQOKHKLpFS8QBA0PgKHFRZVEUhVZEFJ4QPVvCCOUBZFn-tMm6ZD4E5Jz0S92ROlZV4SNbqpvaPCM0t4yZD0JUrmdtgRFBeCR4sU6FMyw3yfLW0-nTkz9Bw7kAB6CsBbJBtXPXLFsh5PRQ07Wcdt5BOLWBDU-XMpE4GWZUwwwoOgMHwYJSFoV6izDTuzK41zsQPBjBR5LjS03wYLWPQcmslVh237FJfKdjj_1c_IzdB8_T7N3vvNsk6pp8fY9K2yFrXfvdPAKR09umgfRfMc-ZV |
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=High-Precision+Inversion+of+Shallow+Bathymetry+under+Complex+Hydrographic+Conditions+Using+VGG19-A+Case+Study+of+the+Taiwan+Banks&rft.jtitle=Remote+sensing+%28Basel%2C+Switzerland%29&rft.au=Jiaxin+Cui&rft.au=Xiaowen+Luo&rft.au=Ziyin+Wu&rft.au=Jieqiong+Zhou&rft.date=2023-03-01&rft.pub=MDPI+AG&rft.issn=2072-4292&rft.eissn=2072-4292&rft.volume=15&rft.issue=5&rft_id=info:doi/10.3390%2Frs15051257&rft.externalDocID=A750512609 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2072-4292&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2072-4292&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2072-4292&client=summon |