CGSANet: A Contour-Guided and Local Structure-Aware Encoder-Decoder Network for Accurate Building Extraction From Very High-Resolution Remote Sensing Imagery

Extracting buildings accurately from very high-resolution (VHR) remote sensing imagery is challenging due to diverse building appearances, spectral variability, and complex background in VHR remote sensing images. Recent studies mainly adopt a variant of the fully convolutional network (FCN) with an...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE journal of selected topics in applied earth observations and remote sensing Jg. 15; S. 1526 - 1542
Hauptverfasser:	Chen, Shanxiong, Shi, Wenzhong, Zhou, Mingting, Zhang, Min, Xuan, Zhaoxin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Boundaries Building extraction Buildings Coders Contours Convolutional neural networks Data mining Datasets Feature extraction fully convolutional network (FCN) High resolution hybrid loss function Image resolution Imagery Localization Modules multitask learning Remote sensing Resolution Semantics Shape very high resolution (VHR) remote sensing imagery
ISSN:	1939-1404, 2151-1535
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	Extracting buildings accurately from very high-resolution (VHR) remote sensing imagery is challenging due to diverse building appearances, spectral variability, and complex background in VHR remote sensing images. Recent studies mainly adopt a variant of the fully convolutional network (FCN) with an encoder-decoder architecture to extract buildings, which has shown promising improvement over conventional methods. However, FCN-based encoder-decoder models still fail to fully utilize the implicit characteristics of building shapes. This adversely affects the accurate localization of building boundaries, which is particularly relevant in building mapping. A contour-guided and local structure-aware encoder-decoder network (CGSANet) is proposed to extract buildings with more accurate boundaries. CGSANet is a multitask network composed of a contour-guided (CG) and a multiregion-guided (MRG) module. The CG module is supervised by a building contour that effectively learns building contour-related spatial features to retain the shape pattern of buildings. The MRG module is deeply supervised by four building regions that further capture multiscale and contextual features of buildings. In addition, a hybrid loss function was designed to improve the structure learning ability of CGSANet. These three improvements benefit each other synergistically to produce high-quality building extraction results. Experimental results on the WHU and NZ32km2 building datasets demonstrate that compared with the tested algorithms, CGSANet can produce more accurate building extraction results and achieve the best intersection over union value 91.55% and 90.02%, respectively. Experiments on the INRIA building dataset further demonstrate the ability for generalization of the proposed framework, indicating great practical potential.
AbstractList	Extracting buildings accurately from very high-resolution (VHR) remote sensing imagery is challenging due to diverse building appearances, spectral variability, and complex background in VHR remote sensing images. Recent studies mainly adopt a variant of the fully convolutional network (FCN) with an encoder-decoder architecture to extract buildings, which has shown promising improvement over conventional methods. However, FCN-based encoder-decoder models still fail to fully utilize the implicit characteristics of building shapes. This adversely affects the accurate localization of building boundaries, which is particularly relevant in building mapping. A contour-guided and local structure-aware encoder-decoder network (CGSANet) is proposed to extract buildings with more accurate boundaries. CGSANet is a multitask network composed of a contour-guided (CG) and a multiregion-guided (MRG) module. The CG module is supervised by a building contour that effectively learns building contour-related spatial features to retain the shape pattern of buildings. The MRG module is deeply supervised by four building regions that further capture multiscale and contextual features of buildings. In addition, a hybrid loss function was designed to improve the structure learning ability of CGSANet. These three improvements benefit each other synergistically to produce high-quality building extraction results. Experimental results on the WHU and NZ32km2 building datasets demonstrate that compared with the tested algorithms, CGSANet can produce more accurate building extraction results and achieve the best intersection over union value 91.55% and 90.02%, respectively. Experiments on the INRIA building dataset further demonstrate the ability for generalization of the proposed framework, indicating great practical potential.
Author	Chen, Shanxiong Shi, Wenzhong Xuan, Zhaoxin Zhou, Mingting Zhang, Min
Author_xml	– sequence: 1 givenname: Shanxiong orcidid: 0000-0002-9235-6340 surname: Chen fullname: Chen, Shanxiong email: shanxiongchen@whu.edu.cn organization: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China – sequence: 2 givenname: Wenzhong orcidid: 0000-0002-3886-7027 surname: Shi fullname: Shi, Wenzhong email: john.wz.shi@polyu.edu.hk organization: Smart Cities Research Institute and Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Hong Kong – sequence: 3 givenname: Mingting orcidid: 0000-0002-5150-4511 surname: Zhou fullname: Zhou, Mingting email: mintyzhou@whu.edu.cn organization: Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan, China – sequence: 4 givenname: Min orcidid: 0000-0003-1643-5271 surname: Zhang fullname: Zhang, Min email: 007zhangmin@whu.edu.cn organization: Smart Cities Research Institute and Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Hong Kong – sequence: 5 givenname: Zhaoxin surname: Xuan fullname: Xuan, Zhaoxin email: 54786060@qq.com organization: Beijing Institute of Surveying and Mapping, Beijing, China
BookMark	eNqFkVFv0zAUhSM0JLrBL9iLJZ7T2bGTxryF0nVFFUjN4NW6tW-KSxoPx9HYj-G_4jTTHnjh6Ur2-c69Oucyuehch0lyzeicMSpvPtf31a6eZzRjc864pGzxKpllLGcpy3l-kcyY5DJlgoo3yWXfHyktsoXks-TPcl1XXzB8IBVZui64wafrwRo0BDpDtk5DS-rgBx0Gj2n1CB7JqtPOoE8_4XmSyD86_5M0zpNK68FDQPJxsK2x3YGsfgcPOljXkVvvTuQ7-idyZw8_0h32rh3OPzs8uQjV2PUjsznBIcreJq8baHt89zyvkm-3q_vlXbr9ut4sq22qBS1DqpuGgWYZNqbIqJBsL3XOOC8LkHuBQhZ5pvfaFFCUCLkGaaTJeGRFjlRTfpVsJl_j4KgevD2Bf1IOrDo_OH9Q4IPVLSqGTMAiKw0TTBR5CaDp3nAGFDTEc6LX-8nrwbtfA_ZBHWOoXTxfZTH0kpeiHFV8Umnv-t5j87KVUTV2qqZO1dipeu40UvIfStsAY4AxYtv-h72eWIuIL9tkUQhelPwvI-SzKQ
CODEN	IJSTHZ
CitedBy_id	crossref_primary_10_1016_j_jag_2023_103522 crossref_primary_10_3390_rs15153766 crossref_primary_10_1109_JSTARS_2024_3387969 crossref_primary_10_1109_LGRS_2024_3432794 crossref_primary_10_1109_TGRS_2024_3459011 crossref_primary_10_3390_electronics13234610 crossref_primary_10_1109_JSTARS_2023_3331444 crossref_primary_10_1007_s41064_025_00352_z crossref_primary_10_1109_TGRS_2025_3540848 crossref_primary_10_3390_rs14081912 crossref_primary_10_1109_LGRS_2025_3562892 crossref_primary_10_1109_JSTARS_2023_3338454 crossref_primary_10_1016_j_asr_2025_08_043 crossref_primary_10_1109_LGRS_2023_3243609 crossref_primary_10_1109_TGRS_2024_3355274 crossref_primary_10_1016_j_jag_2024_104096 crossref_primary_10_1016_j_jag_2022_102824 crossref_primary_10_1109_JSTARS_2024_3454110 crossref_primary_10_1109_TGRS_2024_3383432 crossref_primary_10_3390_electronics12224592 crossref_primary_10_1007_s40747_025_01998_3 crossref_primary_10_3390_buildings14113353 crossref_primary_10_1109_JSTARS_2024_3418387 crossref_primary_10_1016_j_isprsjprs_2024_01_022 crossref_primary_10_1016_j_isprsjprs_2024_02_001 crossref_primary_10_1016_j_apenergy_2024_124172 crossref_primary_10_1016_j_cviu_2024_104253 crossref_primary_10_1109_TGRS_2024_3477290 crossref_primary_10_1016_j_isprsjprs_2023_01_015 crossref_primary_10_3390_en18010119 crossref_primary_10_3389_feart_2023_1268628
Cites_doi	10.3390/rs12091400 10.1016/j.isprsjprs.2013.09.004 10.3390/rs11242970 10.1109/ICIP.2019.8803050 10.1016/j.neucom.2019.11.118 10.3390/rs11070830 10.1109/IGARSS.2019.8900475 10.1609/aaai.v34i07.6916 10.1109/CVPR.2016.90 10.1016/j.isprsjprs.2019.02.019 10.1109/34.922708 10.3390/rs11040403 10.48550/arXiv.1802.02611 10.1109/JSTARS.2018.2865187 10.1109/TGRS.2008.2002027 10.1109/IGARSS.2017.8127684 10.1016/j.isprsjprs.2019.11.023 10.3390/rs10111768 10.5244/C.27.32 10.1016/j.isprsjprs.2020.08.019 10.1007/978-3-030-58520-4_26 10.3390/rs11080917 10.1117/12.2538019 10.1109/TGRS.2020.3006872 10.3390/rs13061049 10.3390/rs10030407 10.14358/pers.77.7.721 10.1109/ICCV.2017.74 10.1109/TGRS.2018.2858817 10.1109/JSTARS.2020.2992298 10.1109/IGARSS.2016.7729471 10.1109/CVPR.2015.7298965 10.1016/j.jag.2013.03.003 10.1109/CVPR.2019.00766 10.1109/IGARSS.2015.7326158 10.1109/TPAMI.2017.2750680 10.1016/j.isprsjprs.2020.01.013 10.1007/978-3-319-24574-4_28 10.1109/JSTARS.2018.2849363 10.1016/j.isprsjprs.2020.09.019 10.3390/rs10071135 10.1109/TGRS.2008.2008440 10.1109/TKDE.2021.3070203 10.5194/isprsarchives-XXXIX-B3-309-2012 10.1109/CVPRW.2018.00045 10.1117/12.2083273 10.1109/TGRS.2020.3026051 10.1109/TGRS.2017.2783902 10.1080/01431161.2018.1528024 10.1007/978-3-319-50835-1_22 10.1109/TGRS.2019.2954461 10.1016/j.isprsjprs.2017.11.009 10.3390/rs10081195 10.1080/01431161.2020.1775322
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022
DBID	97E ESBDL RIA RIE AAYXX CITATION 7UA 8FD C1K F1W FR3 H8D H96 KR7 L.G L7M DOA
DOI	10.1109/JSTARS.2021.3139017
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (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 DOAJ Directory of Open Access Journals
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: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Geology
EISSN	2151-1535
EndPage	1542
ExternalDocumentID	oai_doaj_org_article_1e14a728d1414658aac0bd31a0acac40 10_1109_JSTARS_2021_3139017 9664368
Genre	orig-research
GrantInformation_xml	– fundername: Hong Kong Polytechnic University grantid: 1-ZVN6; ZVU1; 4-BCF7 funderid: 10.13039/501100004377 – fundername: Hong Kong Innovation, and Technology Commission grantid: SST/051/20GP – fundername: Beijing Key Laboratory of Urban Spatial Information Engineering grantid: 2020101
GroupedDBID	0R~ 29I 4.4 5GY 5VS 6IK 97E AAFWJ AAJGR AASAJ AAWTH ABAZT ABVLG ACIWK AENEX AETIX AFPKN AFRAH AGSQL ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ DU5 EBS EJD ESBDL GROUPED_DOAJ HZ~ IFIPE IPLJI JAVBF M43 O9- OCL OK1 RIA RIE RNS AAYXX CITATION 7UA 8FD C1K F1W FR3 H8D H96 KR7 L.G L7M
ID	FETCH-LOGICAL-c408t-cff1ac12efd620491b9c513386a9b4e49652cbcd6a68ea5ca9d9d2340845e0c03
IEDL.DBID	RIE
ISICitedReferencesCount	33
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000754245300001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1939-1404
IngestDate	Fri Oct 03 12:53:02 EDT 2025 Mon Jul 28 15:40:37 EDT 2025 Sat Nov 29 04:51:11 EST 2025 Tue Nov 18 22:14:45 EST 2025 Wed Aug 27 02:49:39 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Language	English
License	https://creativecommons.org/licenses/by/4.0/legalcode
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c408t-cff1ac12efd620491b9c513386a9b4e49652cbcd6a68ea5ca9d9d2340845e0c03
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-3886-7027 0000-0002-5150-4511 0000-0003-1643-5271 0000-0002-9235-6340
OpenAccessLink	https://ieeexplore.ieee.org/document/9664368
PQID	2627838480
PQPubID	75722
PageCount	17
ParticipantIDs	ieee_primary_9664368 crossref_citationtrail_10_1109_JSTARS_2021_3139017 proquest_journals_2627838480 doaj_primary_oai_doaj_org_article_1e14a728d1414658aac0bd31a0acac40 crossref_primary_10_1109_JSTARS_2021_3139017
PublicationCentury	2000
PublicationDate	20220000 2022-00-00 20220101 2022-01-01
PublicationDateYYYYMMDD	2022-01-01
PublicationDate_xml	– year: 2022 text: 20220000
PublicationDecade	2020
PublicationPlace	Piscataway
PublicationPlace_xml	– name: Piscataway
PublicationTitle	IEEE journal of selected topics in applied earth observations and remote sensing
PublicationTitleAbbrev	JSTARS
PublicationYear	2022
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 ref57 ref12 ref56 ref15 ref14 ref53 ref52 ref11 ref10 ref54 ref17 ref16 ref19 ref18 Mnih (ref31) 2013 ref50 ref46 ref45 ref48 ref47 ref42 ref41 ref44 ref43 ref49 Paszke (ref51) 2019; 32 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 ref35 ref34 ref37 ref36 ref30 ref33 ref32 ref2 ref1 ref39 Kingma (ref55) 2015 ref38 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29
References_xml	– ident: ref54 doi: 10.3390/rs12091400 – ident: ref7 doi: 10.1016/j.isprsjprs.2013.09.004 – ident: ref40 doi: 10.3390/rs11242970 – ident: ref42 doi: 10.1109/ICIP.2019.8803050 – ident: ref30 doi: 10.1016/j.neucom.2019.11.118 – ident: ref37 doi: 10.3390/rs11070830 – ident: ref52 doi: 10.1109/IGARSS.2019.8900475 – ident: ref45 doi: 10.1609/aaai.v34i07.6916 – ident: ref26 doi: 10.1109/CVPR.2016.90 – ident: ref3 doi: 10.1016/j.isprsjprs.2019.02.019 – ident: ref1 doi: 10.1109/34.922708 – ident: ref36 doi: 10.3390/rs11040403 – ident: ref18 doi: 10.48550/arXiv.1802.02611 – ident: ref50 doi: 10.1109/JSTARS.2018.2865187 – ident: ref9 doi: 10.1109/TGRS.2008.2002027 – ident: ref12 doi: 10.1109/IGARSS.2017.8127684 – ident: ref28 doi: 10.1016/j.isprsjprs.2019.11.023 – ident: ref39 doi: 10.3390/rs10111768 – ident: ref56 doi: 10.5244/C.27.32 – ident: ref47 doi: 10.1016/j.isprsjprs.2020.08.019 – ident: ref16 doi: 10.1007/978-3-030-58520-4_26 – ident: ref49 doi: 10.3390/rs11080917 – ident: ref43 doi: 10.1117/12.2538019 – ident: ref29 doi: 10.1109/TGRS.2020.3006872 – ident: ref41 doi: 10.3390/rs13061049 – ident: ref33 doi: 10.3390/rs10030407 – ident: ref6 doi: 10.14358/pers.77.7.721 – volume: 32 start-page: 8026 volume-title: Proc. Adv. Neural Inf. Process. Syst. year: 2019 ident: ref51 article-title: PyTorch: An imperative style, high-performance deep learning library – ident: ref57 doi: 10.1109/ICCV.2017.74 – ident: ref13 doi: 10.1109/TGRS.2018.2858817 – ident: ref5 doi: 10.1109/JSTARS.2020.2992298 – year: 2013 ident: ref31 article-title: Machine learning for aerial image labeling – ident: ref35 doi: 10.1109/IGARSS.2016.7729471 – ident: ref14 doi: 10.1109/CVPR.2015.7298965 – ident: ref8 doi: 10.1016/j.jag.2013.03.003 – ident: ref46 doi: 10.1109/CVPR.2019.00766 – ident: ref19 doi: 10.1109/IGARSS.2015.7326158 – ident: ref2 doi: 10.1109/TPAMI.2017.2750680 – ident: ref25 doi: 10.1016/j.isprsjprs.2020.01.013 – ident: ref17 doi: 10.1007/978-3-319-24574-4_28 – ident: ref34 doi: 10.1109/JSTARS.2018.2849363 – ident: ref15 doi: 10.1016/j.isprsjprs.2020.09.019 – ident: ref20 doi: 10.3390/rs10071135 – ident: ref4 doi: 10.1109/TGRS.2008.2008440 – ident: ref44 doi: 10.1109/TKDE.2021.3070203 – ident: ref10 doi: 10.5194/isprsarchives-XXXIX-B3-309-2012 – ident: ref21 doi: 10.1109/CVPRW.2018.00045 – ident: ref32 doi: 10.1117/12.2083273 – ident: ref53 doi: 10.1109/TGRS.2020.3026051 – ident: ref27 doi: 10.1109/TGRS.2017.2783902 – ident: ref38 doi: 10.1080/01431161.2018.1528024 – ident: ref48 doi: 10.1007/978-3-319-50835-1_22 – volume-title: Proc. 3rd Int. Conf. Learn. Representations year: 2015 ident: ref55 article-title: Adam: A method for stochastic optimization – ident: ref11 doi: 10.1109/TGRS.2019.2954461 – ident: ref22 doi: 10.1016/j.isprsjprs.2017.11.009 – ident: ref24 doi: 10.3390/rs10081195 – ident: ref23 doi: 10.1080/01431161.2020.1775322
SSID	ssj0062793
Score	2.4362466
Snippet	Extracting buildings accurately from very high-resolution (VHR) remote sensing imagery is challenging due to diverse building appearances, spectral...
SourceID	doaj proquest crossref ieee
SourceType	Open Website Aggregation Database Enrichment Source Index Database Publisher
StartPage	1526
SubjectTerms	Algorithms Boundaries Building extraction Buildings Coders Contours Convolutional neural networks Data mining Datasets Feature extraction fully convolutional network (FCN) High resolution hybrid loss function Image resolution Imagery Localization Modules multitask learning Remote sensing Resolution Semantics Shape very high resolution (VHR) remote sensing imagery
SummonAdditionalLinks	– databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Bi9QwFA6yKHgRdRVHV8nBo2GbNG2TvXXXmVFYBtnRZW8hTV5hwe0u3RnX-TH-V99LO4Mi6MVToSRpyfvS5Etfvo-xt7nONVTWCGRXQeimiMI0xooiNqVSsm2y9Pf8_LRaLMzFhf30i9UX5YQN8sBDxx1KkNpXykSpcVAXxvuQNTGXPvPBB53YOq56tmRq-AaXCmE3agzJzB4iyOuzJbJBJZGkEs2vfpuHklz_6K_yx0c5zTSzx-zRuETk9fBqT9g96J6yB_NkwbvZZz9O5st6AasjXnPSlsLiYr6-jBC57yI_pcmJL5Ms7LoHUd_5Hvi0o7PrvXgP6coXQ_Y3xyUrr0NYk2AEPx4tsvn0-6ofDjzwWX99xc-h33DKCBG02z9glZ8BRhn4kjLgsc7HK1LD2DxjX2bTzycfxGiyILDzzEqEtpU-SAVtJGl6KxsbyPPFlN42GkhOXoUmxNKXBnwRvI02qhzr6gKykOXP2V533cELxtvKqgzAGIy_zkttwcgWG_V5wIVhKCdMbbvchVGBnIwwvrrERDLrhjg5ipMb4zRh73aVbgYBjr8XP6ZY7oqSena6gZhyI6bcvzA1YfuEhF0jSAlJqH_CDrbIcONIv3WqJK8So0328n88-hV7qOiARdrkOWB7iBZ4ze6Hb6vL2_5NAvlP7Fr-WQ priority: 102 providerName: Directory of Open Access Journals
Title	CGSANet: A Contour-Guided and Local Structure-Aware Encoder-Decoder Network for Accurate Building Extraction From Very High-Resolution Remote Sensing Imagery
URI	https://ieeexplore.ieee.org/document/9664368 https://www.proquest.com/docview/2627838480 https://doaj.org/article/1e14a728d1414658aac0bd31a0acac40
Volume	15
WOSCitedRecordID	wos000754245300001&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2151-1535 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0062793 issn: 1939-1404 databaseCode: DOA dateStart: 20200101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 2151-1535 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0062793 issn: 1939-1404 databaseCode: RIE dateStart: 20080101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fb9MwELa2CSRexo-BKIzJDzzOzHacxOYtG21Bmiq0wrS3yLEv0qQtRVk76B_D_4rPcSshEBJPiSJfFPm--O7su-8IeZupTEFpNAvRlWOqyT3TjTYs900hpWgbHk_PL8_L2UxfXZnPO-R4WwsDADH5DN7hbTzL9wu3wq2yk-CaI2H6Ltkty3Ko1dqsuoUsI8Fu8EcMQ8qYxDAkuDkJEK8u5iEWlCKEqBjkl79ZoUjWn7qr_LEkRzszefx_X_iE7Cd_klYDAJ6SHeiekYfT2K93fUB-nk3n1QyW72lFkYgqDGfT1bUHT23n6TlaMjqPHLKrHlj13fZAxx0WuvfsA8QrnQ2p4jT4t7RyboXsEvQ09dOm4x_LfqiOoJN-cUsvoV9TTB9heDQwAJteQIAE0DmmyweZT7dInbF-Tr5Oxl_OPrLUkYE5xfWSubYV1gkJrUceeyMa47BBjC6saRQg97x0jfOFLTTY3FnjjZdZkFU5cMezF2SvW3TwktC2NJIDaB3AEqZMGdCiDS-1mQtepCtGRG40VLtEV45dM27qGLZwUw9qrVGtdVLriBxvhb4NbB3_Hn6Kqt8ORart-CDotE5_bi1AKFtK7YUKViXX1jre-ExYbp0N0zIiB4iD7UsSBEbkcAOkOi0Ld7UssLGJVpq_-rvUa_JIYn1F3OM5JHtB__CGPHD3y-u7_ihuGBxF3P8CGWz-PA
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fb9MwELbGALEXfm1ohQF-4HFmtuMkNm_ZaLuJUqF1THuLHPsiTWIpylqgfwz_Kz4nrYRASDwlinxR5Pviu7PvviPkTaISBbnRLERXjqkq9UxX2rDUV5mUoq54PD2_nOTTqb66Mp-2yOGmFgYAYvIZvMXbeJbv526JW2VHwTVHwvQ75G6qlBRdtdZ63c1kHil2g0diGJLG9BxDgpujAPLifBaiQSlCkIphfv6bHYp0_X1_lT8W5WhpRo_-7xsfk4e9R0mLDgJPyBY0T8n9cezYu9olP0_Gs2IKi3e0oEhFFYaz8fLag6e28XSCtozOIovssgVWfLct0GGDpe4tew_xSqddsjgNHi4tnFsivwQ97jtq0-GPRdvVR9BRO7-hl9CuKCaQMDwc6KBNzyGAAugME-aDzNkNkmes9sjn0fDi5JT1PRmYU1wvmKtrYZ2QUHtksjeiMg5bxOjMmkoBss9LVzmf2UyDTZ013niZBFmVAnc8eUa2m3kD-4TWuZEcQOsAlzBlyoAWdXipTVzwI102IHKtodL1hOXYN-NLGQMXbspOrSWqtezVOiCHG6GvHV_Hv4cfo-o3Q5FsOz4IOi37f7cUIJTNpfZCBbuSamsdr3wiLLfOhmkZkF3EweYlPQQG5GANpLJfGG5LmWFrE600f_53qdfkwenFx0k5OZt-eEF2JFZbxB2fA7IdsAAvyT33bXF9276K6P8FcvIAnQ
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=CGSANet%3A+A+Contour-Guided+and+Local+Structure-Aware+Encoder-Decoder+Network+for+Accurate+Building+Extraction+From+Very+High-Resolution+Remote+Sensing+Imagery&rft.jtitle=IEEE+journal+of+selected+topics+in+applied+earth+observations+and+remote+sensing&rft.au=Chen%2C+Shanxiong&rft.au=Shi%2C+Wenzhong&rft.au=Zhou%2C+Mingting&rft.au=Zhang%2C+Min&rft.date=2022&rft.pub=IEEE&rft.issn=1939-1404&rft.volume=15&rft.spage=1526&rft.epage=1542&rft_id=info:doi/10.1109%2FJSTARS.2021.3139017&rft.externalDocID=9664368
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1939-1404&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1939-1404&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1939-1404&client=summon