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Unified Land–Ocean Quasi-Geoid Computation from Heterogeneous Data Sets Based on Radial Basis Functions
The determination of the land geoid and the marine geoid involves different data sets and calculation strategies. It is a hot issue at present to construct the unified land–ocean quasi-geoid by fusing multi-source data in coastal areas, which is of great significance to the construction of land–ocea...
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Published in: | Remote sensing (Basel, Switzerland) Switzerland), 2022-07, Vol.14 (13), p.3015 |
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description | The determination of the land geoid and the marine geoid involves different data sets and calculation strategies. It is a hot issue at present to construct the unified land–ocean quasi-geoid by fusing multi-source data in coastal areas, which is of great significance to the construction of land–ocean integration. Classical geoid integral algorithms such as the Stokes theory find it difficult to deal with heterogeneous gravity signals, so scholars have gradually begun using radial basis functions (RBFs) to fuse multi-source data. This article designs a multi-layer RBF network to construct the unified land–ocean quasi-geoid fusing measured terrestrial, shipborne, satellite altimetry and airborne gravity data based on the Remove–Compute–Restore (RCR) technique. EIGEN-6C4 of degree 2190 is used as a reference gravity field. Several core problems in the process of RBF modeling are studied in depth: (1) the behavior of RBFs in the spatial domain; (2) the locations of RBFs; (3) ill-conditioned problems of the design matrix; (4) the effect of terrain masses. The local quasi-geoid with a 1′ resolution is calculated, respectively, on the flat east coast and the rugged west coast of the United States. The results show that the accuracy of the quasi-geoid computed by fusing four types of gravity data in the east coast experimental area is 1.9 cm inland and 1.3 cm on coast after internal verification (the standard deviation of the quasi-geoid w.r.t GPS/leveling data). The accuracy of the quasi-geoid calculated in the west coast experimental area is 2.2 cm inland and 2.1 cm on coast. The results indicate that using RBFs to calculate the unified land–ocean quasi-geoid from heterogeneous data sets has important application value. |
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It is a hot issue at present to construct the unified land–ocean quasi-geoid by fusing multi-source data in coastal areas, which is of great significance to the construction of land–ocean integration. Classical geoid integral algorithms such as the Stokes theory find it difficult to deal with heterogeneous gravity signals, so scholars have gradually begun using radial basis functions (RBFs) to fuse multi-source data. This article designs a multi-layer RBF network to construct the unified land–ocean quasi-geoid fusing measured terrestrial, shipborne, satellite altimetry and airborne gravity data based on the Remove–Compute–Restore (RCR) technique. EIGEN-6C4 of degree 2190 is used as a reference gravity field. Several core problems in the process of RBF modeling are studied in depth: (1) the behavior of RBFs in the spatial domain; (2) the locations of RBFs; (3) ill-conditioned problems of the design matrix; (4) the effect of terrain masses. The local quasi-geoid with a 1′ resolution is calculated, respectively, on the flat east coast and the rugged west coast of the United States. The results show that the accuracy of the quasi-geoid computed by fusing four types of gravity data in the east coast experimental area is 1.9 cm inland and 1.3 cm on coast after internal verification (the standard deviation of the quasi-geoid w.r.t GPS/leveling data). The accuracy of the quasi-geoid calculated in the west coast experimental area is 2.2 cm inland and 2.1 cm on coast. The results indicate that using RBFs to calculate the unified land–ocean quasi-geoid from heterogeneous data sets has important application value.</description><identifier>ISSN: 2072-4292</identifier><identifier>EISSN: 2072-4292</identifier><identifier>DOI: 10.3390/rs14133015</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Accuracy ; Airborne sensing ; Algorithms ; Approximation ; Coastal zone ; Datasets ; EIGEN-6C4 ; Experiments ; fusion of heterogeneous data ; Geoids ; Global positioning systems ; GPS ; Gravitational fields ; Gravity ; Ill-conditioned problems (mathematics) ; Microprocessors ; Multilayers ; Parameter estimation ; Radial basis function ; radial basis functions ; Remote sensing ; Remove–Compute–Restore ; Satellite altimetry ; Statistical methods ; unified land–ocean quasi-geoid</subject><ispartof>Remote sensing (Basel, Switzerland), 2022-07, Vol.14 (13), p.3015</ispartof><rights>2022 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.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c291t-fdc762f784fa68b5f4344f7b3be45a451378760709ff6ea8a0f890001dcc1f33</citedby><cites>FETCH-LOGICAL-c291t-fdc762f784fa68b5f4344f7b3be45a451378760709ff6ea8a0f890001dcc1f33</cites><orcidid>0000-0002-3525-1817</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2686171282/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2686171282?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,27924,27925,37012,44590,75126</link.rule.ids></links><search><creatorcontrib>Liu, Yusheng</creatorcontrib><creatorcontrib>Lou, Lizhi</creatorcontrib><title>Unified Land–Ocean Quasi-Geoid Computation from Heterogeneous Data Sets Based on Radial Basis Functions</title><title>Remote sensing (Basel, Switzerland)</title><description>The determination of the land geoid and the marine geoid involves different data sets and calculation strategies. It is a hot issue at present to construct the unified land–ocean quasi-geoid by fusing multi-source data in coastal areas, which is of great significance to the construction of land–ocean integration. Classical geoid integral algorithms such as the Stokes theory find it difficult to deal with heterogeneous gravity signals, so scholars have gradually begun using radial basis functions (RBFs) to fuse multi-source data. This article designs a multi-layer RBF network to construct the unified land–ocean quasi-geoid fusing measured terrestrial, shipborne, satellite altimetry and airborne gravity data based on the Remove–Compute–Restore (RCR) technique. EIGEN-6C4 of degree 2190 is used as a reference gravity field. Several core problems in the process of RBF modeling are studied in depth: (1) the behavior of RBFs in the spatial domain; (2) the locations of RBFs; (3) ill-conditioned problems of the design matrix; (4) the effect of terrain masses. The local quasi-geoid with a 1′ resolution is calculated, respectively, on the flat east coast and the rugged west coast of the United States. The results show that the accuracy of the quasi-geoid computed by fusing four types of gravity data in the east coast experimental area is 1.9 cm inland and 1.3 cm on coast after internal verification (the standard deviation of the quasi-geoid w.r.t GPS/leveling data). The accuracy of the quasi-geoid calculated in the west coast experimental area is 2.2 cm inland and 2.1 cm on coast. The results indicate that using RBFs to calculate the unified land–ocean quasi-geoid from heterogeneous data sets has important application value.</description><subject>Accuracy</subject><subject>Airborne sensing</subject><subject>Algorithms</subject><subject>Approximation</subject><subject>Coastal zone</subject><subject>Datasets</subject><subject>EIGEN-6C4</subject><subject>Experiments</subject><subject>fusion of heterogeneous data</subject><subject>Geoids</subject><subject>Global positioning systems</subject><subject>GPS</subject><subject>Gravitational fields</subject><subject>Gravity</subject><subject>Ill-conditioned problems (mathematics)</subject><subject>Microprocessors</subject><subject>Multilayers</subject><subject>Parameter estimation</subject><subject>Radial basis function</subject><subject>radial basis functions</subject><subject>Remote sensing</subject><subject>Remove–Compute–Restore</subject><subject>Satellite altimetry</subject><subject>Statistical methods</subject><subject>unified land–ocean quasi-geoid</subject><issn>2072-4292</issn><issn>2072-4292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpNkc1qGzEQx5eSQk2SS59A0FtgU42klbTH1E3igMG0dc9iVh9Gxl650u4ht7xD3rBP0nVcksxlPvjPb2aYqvoM9Jrzln7NBQRwTqH5UM0YVawWrGVn7-JP1WUpWzoZ59BSMavi7z6G6B1ZYu_-Pj2vrMee_BixxPrep-jIPO0P44BDTD0JOe3Jwg8-p43vfRoL-Y4Dkl9-KOQblokzqX6ii7g75rGQu7G3x95yUX0MuCv-8r8_r9Z3t-v5ol6u7h_mN8vashaGOjirJAtKi4BSd00QXIigOt550aBogCutJFW0DUF61EiDbqeDwFkLgfPz6uGEdQm35pDjHvOjSRjNSyHljcE8RLvzxmvbgWqlZboRiFwLqSQwAOWCYxom1pcT65DTn9GXwWzTmPtpe8OklqCAaTaprk4qm1Mp2YfXqUDN8THm7TH8H628f4A</recordid><startdate>20220701</startdate><enddate>20220701</enddate><creator>Liu, Yusheng</creator><creator>Lou, Lizhi</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7QR</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PCBAR</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-3525-1817</orcidid></search><sort><creationdate>20220701</creationdate><title>Unified Land–Ocean Quasi-Geoid Computation from Heterogeneous Data Sets Based on Radial Basis Functions</title><author>Liu, Yusheng ; Lou, Lizhi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-fdc762f784fa68b5f4344f7b3be45a451378760709ff6ea8a0f890001dcc1f33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Accuracy</topic><topic>Airborne sensing</topic><topic>Algorithms</topic><topic>Approximation</topic><topic>Coastal zone</topic><topic>Datasets</topic><topic>EIGEN-6C4</topic><topic>Experiments</topic><topic>fusion of heterogeneous data</topic><topic>Geoids</topic><topic>Global positioning systems</topic><topic>GPS</topic><topic>Gravitational fields</topic><topic>Gravity</topic><topic>Ill-conditioned problems (mathematics)</topic><topic>Microprocessors</topic><topic>Multilayers</topic><topic>Parameter estimation</topic><topic>Radial basis function</topic><topic>radial basis functions</topic><topic>Remote sensing</topic><topic>Remove–Compute–Restore</topic><topic>Satellite altimetry</topic><topic>Statistical methods</topic><topic>unified land–ocean quasi-geoid</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Yusheng</creatorcontrib><creatorcontrib>Lou, Lizhi</creatorcontrib><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Ecology Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>SciTech Premium Collection</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>Publicly Available Content Database (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Open Access: DOAJ - Directory of Open Access Journals</collection><jtitle>Remote sensing (Basel, Switzerland)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Yusheng</au><au>Lou, Lizhi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Unified Land–Ocean Quasi-Geoid Computation from Heterogeneous Data Sets Based on Radial Basis Functions</atitle><jtitle>Remote sensing (Basel, Switzerland)</jtitle><date>2022-07-01</date><risdate>2022</risdate><volume>14</volume><issue>13</issue><spage>3015</spage><pages>3015-</pages><issn>2072-4292</issn><eissn>2072-4292</eissn><abstract>The determination of the land geoid and the marine geoid involves different data sets and calculation strategies. It is a hot issue at present to construct the unified land–ocean quasi-geoid by fusing multi-source data in coastal areas, which is of great significance to the construction of land–ocean integration. Classical geoid integral algorithms such as the Stokes theory find it difficult to deal with heterogeneous gravity signals, so scholars have gradually begun using radial basis functions (RBFs) to fuse multi-source data. This article designs a multi-layer RBF network to construct the unified land–ocean quasi-geoid fusing measured terrestrial, shipborne, satellite altimetry and airborne gravity data based on the Remove–Compute–Restore (RCR) technique. EIGEN-6C4 of degree 2190 is used as a reference gravity field. Several core problems in the process of RBF modeling are studied in depth: (1) the behavior of RBFs in the spatial domain; (2) the locations of RBFs; (3) ill-conditioned problems of the design matrix; (4) the effect of terrain masses. The local quasi-geoid with a 1′ resolution is calculated, respectively, on the flat east coast and the rugged west coast of the United States. The results show that the accuracy of the quasi-geoid computed by fusing four types of gravity data in the east coast experimental area is 1.9 cm inland and 1.3 cm on coast after internal verification (the standard deviation of the quasi-geoid w.r.t GPS/leveling data). The accuracy of the quasi-geoid calculated in the west coast experimental area is 2.2 cm inland and 2.1 cm on coast. The results indicate that using RBFs to calculate the unified land–ocean quasi-geoid from heterogeneous data sets has important application value.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/rs14133015</doi><orcidid>https://orcid.org/0000-0002-3525-1817</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Accuracy Airborne sensing Algorithms Approximation Coastal zone Datasets EIGEN-6C4 Experiments fusion of heterogeneous data Geoids Global positioning systems GPS Gravitational fields Gravity Ill-conditioned problems (mathematics) Microprocessors Multilayers Parameter estimation Radial basis function radial basis functions Remote sensing Remove–Compute–Restore Satellite altimetry Statistical methods unified land–ocean quasi-geoid |
title | Unified Land–Ocean Quasi-Geoid Computation from Heterogeneous Data Sets Based on Radial Basis Functions |
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