AIR-PolSAR-Seg: A Large-Scale Data Set for Terrain Segmentation in Complex-Scene PolSAR Images

Polarimetric synthetic aperture radar (PolSAR) terrain segmentation is a fundamental research topic in PolSAR image interpretation. Recently, many studies have been investigated to handle this task. However, the existing data for PolSAR terrain segmentation have relatively limited scale and their sc...

Full description

Saved in:

Bibliographic Details
Published in:	IEEE journal of selected topics in applied earth observations and remote sensing Vol. 15; pp. 3830 - 3841
Main Authors:	Wang, Zhirui, Zeng, Xuan, Yan, Zhiyuan, Kang, Jian, Sun, Xian
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Air Algorithms Annotations Benchmark data set Complexity theory Datasets Deep learning Image analysis Image color analysis Image processing Image segmentation Machine learning Pixels polarimetric synthetic aperture radar (PolSAR) Radar imaging SAR (radar) Spatial resolution Synthetic aperture radar Task analysis Task complexity Terrain terrain segmentation Training Water bodies
ISSN:	1939-1404, 2151-1535
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Polarimetric synthetic aperture radar (PolSAR) terrain segmentation is a fundamental research topic in PolSAR image interpretation. Recently, many studies have been investigated to handle this task. However, the existing data for PolSAR terrain segmentation have relatively limited scale and their scene complexity is relatively simple. These issues greatly restrict the development of algorithms. Therefore, there is a strong requirement for establishing a large-scale data set for terrain segmentation in complex-scene PolSAR images. In this paper, we present a benchmark data set containing a PolSAR amplitude image with a 9082×9805-pixel region and 2000 image patches with a size of 512×512 for PolSAR terrain segmentation, which is called AIR-PolSAR-Seg. We collect the PolSAR image with a resolution of 8 m from the GaoFen-3 satellite, and it is equipped with pixel-wise annotation which covers six categories. Compared with the previous data resources, AIR-PolSAR-Seg preserves some specific properties. First, AIR-PolSAR-Seg owns a large-size PolSAR image and provides a large quantity of image patches. It offers the research community a complete data resource with adequate training examples and reliable validation results. Second, AIR-PolSAR-Seg is established upon a PolSAR image with high scene complexity. This characteristic motivates robust and advanced segmentation approaches to facilitate complex-scene PolSAR image analysis. Based on AIR-PolSAR-Seg, three tasks are introduced: multi-category segmentation, water body segmentation, and building segmentation. Moreover, a performance analysis of traditional approaches and deep learning-based approaches are conducted, which can be regarded as baselines and provide references for future research.
AbstractList	Polarimetric synthetic aperture radar (PolSAR) terrain segmentation is a fundamental research topic in PolSAR image interpretation. Recently, many studies have been investigated to handle this task. However, the existing data for PolSAR terrain segmentation have relatively limited scale and their scene complexity is relatively simple. These issues greatly restrict the development of algorithms. Therefore, there is a strong requirement for establishing a large-scale data set for terrain segmentation in complex-scene PolSAR images. In this paper, we present a benchmark data set containing a PolSAR amplitude image with a 9082×9805-pixel region and 2000 image patches with a size of 512×512 for PolSAR terrain segmentation, which is called AIR-PolSAR-Seg. We collect the PolSAR image with a resolution of 8 m from the GaoFen-3 satellite, and it is equipped with pixel-wise annotation which covers six categories. Compared with the previous data resources, AIR-PolSAR-Seg preserves some specific properties. First, AIR-PolSAR-Seg owns a large-size PolSAR image and provides a large quantity of image patches. It offers the research community a complete data resource with adequate training examples and reliable validation results. Second, AIR-PolSAR-Seg is established upon a PolSAR image with high scene complexity. This characteristic motivates robust and advanced segmentation approaches to facilitate complex-scene PolSAR image analysis. Based on AIR-PolSAR-Seg, three tasks are introduced: multi-category segmentation, water body segmentation, and building segmentation. Moreover, a performance analysis of traditional approaches and deep learning-based approaches are conducted, which can be regarded as baselines and provide references for future research.
Author	Yan, Zhiyuan Wang, Zhirui Zeng, Xuan Sun, Xian Kang, Jian
Author_xml	– sequence: 1 givenname: Zhirui orcidid: 0000-0003-2877-0384 surname: Wang fullname: Wang, Zhirui email: zhirui1990@126.com organization: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China – sequence: 2 givenname: Xuan orcidid: 0000-0001-5047-3488 surname: Zeng fullname: Zeng, Xuan email: sunxian@aircas.ac.cn organization: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China – sequence: 3 givenname: Zhiyuan orcidid: 0000-0002-4264-6868 surname: Yan fullname: Yan, Zhiyuan email: yanzy@aircas.ac.cn organization: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China – sequence: 4 givenname: Jian orcidid: 0000-0001-6284-3044 surname: Kang fullname: Kang, Jian email: kangjian_1991@outlook.com organization: School of Electronic and Information Engineering, Soochow University, Suzhou, China – sequence: 5 givenname: Xian orcidid: 0000-0002-0038-9816 surname: Sun fullname: Sun, Xian email: zengxuan19@mails.ucas.ac.cn organization: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
BookMark	eNqFkU1v2zAMhoWiA5Z2-wW9CNjZmST6S70Z2VeGAC3i7DqBlinDgWNlsgt0_37KXPSwy04Eifd5SfC9YdejH4mxOynWUgr98Xt9qPb1Wgml1iALASq_YislM5nIDLJrtpIadCJTkb5lN9N0FCJXhYYV-1lt98mjH-pqn9TU3fOK7zB0lNQWB-KfcEZe08ydD_xAIWA_xr470Tjj3PuRx37jT-eBniNCI_HFjG9P2NH0jr1xOEz0_qXesh9fPh8235Ldw9ftptolNhXlnKQKoXW5tpIyFNQCOu0Kl7fOocraVmMD0qUIWVpAgWDTeH4JWSN02QhUcMu2i2_r8WjOoT9h-G089ubvwIfOYJh7O5Cxsk2bworG5ZCWoHQrpby4QVNaq4vo9WHxOgf_64mm2Rz9Uxjj-UbleSGiXIuogkVlg5-mQO51qxTmEopZQjGXUMxLKJHS_1C2Xx45x9cO_2HvFrYnotdtusgzKDX8Abb2mfw
CODEN	IJSTHZ
CitedBy_id	crossref_primary_10_1109_TAES_2024_3382622 crossref_primary_10_1109_TGRS_2022_3204705 crossref_primary_10_1109_TGRS_2023_3332219 crossref_primary_10_1109_TGRS_2024_3517878 crossref_primary_10_1109_TGRS_2025_3603998 crossref_primary_10_1016_j_patcog_2025_111895 crossref_primary_10_1109_LGRS_2025_3568837 crossref_primary_10_3390_rs15143662 crossref_primary_10_1016_j_isprsjprs_2025_08_023 crossref_primary_10_1109_JSTARS_2025_3600810 crossref_primary_10_1109_TGRS_2024_3517594 crossref_primary_10_1109_TAES_2024_3388373 crossref_primary_10_3390_rs16071150 crossref_primary_10_3390_app13042520 crossref_primary_10_1016_j_ophoto_2023_100047 crossref_primary_10_1109_JSTARS_2025_3579753 crossref_primary_10_1109_TGRS_2023_3333431 crossref_primary_10_3390_electronics14040791 crossref_primary_10_3390_rs15194802 crossref_primary_10_3390_rs16020296 crossref_primary_10_1109_TGRS_2025_3607445
Cites_doi	10.1109/TGRS.2019.2954328 10.1109/TGRS.2012.2194787 10.1109/JSTARS.2017.2728067 10.1109/LGRS.2020.3038240 10.1109/TGRS.2018.2879984 10.1109/TGRS.2021.3130174 10.1109/TGRS.2019.2952236 10.1109/LGRS.2018.2833492 10.1109/TGRS.2007.905103 10.1109/TGRS.2014.2349575 10.1109/ICCV.2019.00068 10.1109/TGRS.2019.2923738 10.1016/j.isprsjprs.2018.02.006 10.1109/CVPR.2017.660 10.1109/JSTARS.2021.3076085 10.1109/CVPR.2015.7298965 10.1109/LGRS.2019.2953203 10.1007/3-540-45054-8_27 10.1162/neco.1989.1.4.541 10.1109/CVPR.2018.00747 10.1109/TGRS.2012.2203358 10.1023/b:visi.0000029664.99615.94 10.1109/JSTARS.2020.3019418 10.1109/LGRS.2018.2886559 10.1109/TGRS.2017.2675906 10.1109/ICCVW.2019.00246 10.1109/TGRS.2019.2926434 10.1109/ICCV.2019.00926 10.1109/TIP.2020.2992177 10.1109/TGRS.2020.3012276 10.1109/CVPR.2016.90 10.1109/CVPR.2019.00326 10.1007/978-3-030-01234-2_49 10.1109/TGRS.2019.2949066 10.1007/s12524-018-0891-y 10.1109/TGRS.2021.3079438 10.1109/JSTARS.2021.3140101 10.1109/IVS.2010.5547996 10.1109/LGRS.2021.3079925 10.1109/JSTARS.2018.2873417 10.1109/JSTARS.2015.2492552 10.1109/JSTARS.2021.3116062 10.1109/IGARSS.2019.8900267 10.3390/rs13163132 10.1007/978-3-030-01240-3_17 10.1007/978-3-031-14903-0_23 10.1109/TGRS.2020.3023928 10.1109/TGRS.2020.3020165 10.1109/IASP.2009.5054605 10.1109/ICCV.2019.00069 10.1109/CVPR.2018.00813 10.1109/TGRS.2020.3005151 10.1109/TNNLS.2018.2885799 10.1109/TNNLS.2020.2979546 10.1109/JSTARS.2021.3063797 10.1109/TGRS.2017.2728186 10.1109/TGRS.2017.2685945
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.2022.3170326
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/IET Electronic Library url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Geology
EISSN	2151-1535
EndPage	3841
ExternalDocumentID	oai_doaj_org_article_c1d4b7c0bf6348329d11127833b8cc97 10_1109_JSTARS_2022_3170326 9765389
Genre	orig-research
GrantInformation_xml	– fundername: National Natural Science Foundation of China grantid: 61725105; 62076241; 62171436; 62101371 funderid: 10.13039/501100001809 – fundername: Jiangsu Province Science Foundation for Youths grantid: BK20210707
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-42a3df69c1e5a0ed3af9f7f6dffa25dd9ab31f4a354737a3c4627835b098b0a23
IEDL.DBID	DOA
ISICitedReferencesCount	26
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000798194300010&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 12:50:03 EDT 2025 Mon Jul 28 14:10:38 EDT 2025 Sat Nov 29 04:51:12 EST 2025 Tue Nov 18 21:51:47 EST 2025 Wed Aug 27 02:37:55 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-42a3df69c1e5a0ed3af9f7f6dffa25dd9ab31f4a354737a3c4627835b098b0a23
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0001-6284-3044 0000-0001-5047-3488 0000-0002-4264-6868 0000-0002-0038-9816 0000-0003-2877-0384
OpenAccessLink	https://doaj.org/article/c1d4b7c0bf6348329d11127833b8cc97
PQID	2667011190
PQPubID	75722
PageCount	12
ParticipantIDs	ieee_primary_9765389 doaj_primary_oai_doaj_org_article_c1d4b7c0bf6348329d11127833b8cc97 crossref_primary_10_1109_JSTARS_2022_3170326 proquest_journals_2667011190 crossref_citationtrail_10_1109_JSTARS_2022_3170326
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 ref59 ref14 ref58 ref53 ref52 ref11 ref55 ref10 ref54 ref16 ref19 ref18 Mohammadimanesh (ref17) 2019; 151 ref51 ref50 ref46 ref45 ref47 ref42 Xie (ref3) 2020; 388 ref41 ref44 ref43 ref8 ref7 ref9 ref4 ref5 ref40 Sun (ref34) 2021; 173 ref35 Sun (ref30) 2022; 184 ref37 ref36 ref31 ref33 ref32 Contributors (ref61) 2020 ref2 ref1 ref39 ref38 Liu (ref6) 2016; 59 ref24 ref23 ref26 ref25 ref20 ref64 ref63 ref22 ref21 ref65 ref28 ref27 ref29 Suykens (ref48) 1999; 9 ref60 ref62 M (ref49) 2005; 26
References_xml	– volume: 9 start-page: 293 issue: 3 volume-title: Neural Process. Lett. year: 1999 ident: ref48 article-title: Least squares support vector machine classifiers – ident: ref32 doi: 10.1109/TGRS.2019.2954328 – year: 2020 ident: ref61 article-title: MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark – ident: ref15 doi: 10.1109/TGRS.2012.2194787 – ident: ref41 doi: 10.1109/JSTARS.2017.2728067 – ident: ref42 doi: 10.1109/LGRS.2020.3038240 – ident: ref5 doi: 10.1109/TGRS.2018.2879984 – ident: ref25 doi: 10.1109/TGRS.2021.3130174 – ident: ref4 doi: 10.1109/TGRS.2019.2952236 – ident: ref20 doi: 10.1109/LGRS.2018.2833492 – volume: 173 start-page: 50 volume-title: ISPRS J. Photogrammetry Remote Sens. year: 2021 ident: ref34 article-title: PBNet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery – ident: ref8 doi: 10.1109/TGRS.2007.905103 – ident: ref21 doi: 10.1109/TGRS.2014.2349575 – ident: ref53 doi: 10.1109/ICCV.2019.00068 – ident: ref23 doi: 10.1109/TGRS.2019.2923738 – ident: ref39 doi: 10.1016/j.isprsjprs.2018.02.006 – ident: ref51 doi: 10.1109/CVPR.2017.660 – ident: ref64 doi: 10.1109/JSTARS.2021.3076085 – ident: ref50 doi: 10.1109/CVPR.2015.7298965 – ident: ref12 doi: 10.1109/LGRS.2019.2953203 – ident: ref47 doi: 10.1007/3-540-45054-8_27 – ident: ref62 doi: 10.1162/neco.1989.1.4.541 – ident: ref58 doi: 10.1109/CVPR.2018.00747 – ident: ref9 doi: 10.1109/TGRS.2012.2203358 – ident: ref45 doi: 10.1023/b:visi.0000029664.99615.94 – ident: ref65 doi: 10.1109/JSTARS.2020.3019418 – ident: ref18 doi: 10.1109/LGRS.2018.2886559 – volume: 59 start-page: 325 volume-title: Pattern Recognit. year: 2016 ident: ref6 article-title: Hierarchical semantic model and scattering mechanism based PolSAR image classification – volume: 151 start-page: 223 volume-title: ISPRS J. Photogrammetry Remote Sens. year: 2019 ident: ref17 article-title: A new fully convolutional neural network for semantic segmentation of polarimetric SAR imagery in complex land cover ecosystem – ident: ref10 doi: 10.1109/TGRS.2017.2675906 – volume: 184 volume-title: ISPRS J. Photogrammetry Remote Sens. year: 2022 ident: ref30 article-title: FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery – ident: ref60 doi: 10.1109/ICCVW.2019.00246 – ident: ref22 doi: 10.1109/TGRS.2019.2926434 – ident: ref57 doi: 10.1109/ICCV.2019.00926 – ident: ref19 doi: 10.1109/TIP.2020.2992177 – ident: ref7 doi: 10.1109/TGRS.2020.3012276 – ident: ref36 doi: 10.1109/CVPR.2016.90 – ident: ref55 doi: 10.1109/CVPR.2019.00326 – volume: 26 start-page: 217 issue: 1 volume-title: Int. J. Remote Sens. year: 2005 ident: ref49 article-title: Random forest classifier for remote sensing classification – ident: ref52 doi: 10.1007/978-3-030-01234-2_49 – ident: ref26 doi: 10.1109/TGRS.2019.2949066 – ident: ref43 doi: 10.1007/s12524-018-0891-y – ident: ref13 doi: 10.1109/TGRS.2021.3079438 – ident: ref27 doi: 10.1109/JSTARS.2021.3140101 – volume: 388 start-page: 255 volume-title: Neurocomputing year: 2020 ident: ref3 article-title: PolSAR image classification via a novel semi-supervised recurrent complex-valued convolution neural network – ident: ref46 doi: 10.1109/IVS.2010.5547996 – ident: ref35 doi: 10.1109/LGRS.2021.3079925 – ident: ref2 doi: 10.1109/JSTARS.2018.2873417 – ident: ref16 doi: 10.1109/JSTARS.2015.2492552 – ident: ref24 doi: 10.1109/JSTARS.2021.3116062 – ident: ref40 doi: 10.1109/IGARSS.2019.8900267 – ident: ref14 doi: 10.3390/rs13163132 – ident: ref59 doi: 10.1007/978-3-030-01240-3_17 – ident: ref29 doi: 10.1007/978-3-031-14903-0_23 – ident: ref33 doi: 10.1109/TGRS.2020.3023928 – ident: ref37 doi: 10.1109/TGRS.2020.3020165 – ident: ref44 doi: 10.1109/IASP.2009.5054605 – ident: ref54 doi: 10.1109/ICCV.2019.00069 – ident: ref56 doi: 10.1109/CVPR.2018.00813 – ident: ref1 doi: 10.1109/TGRS.2020.3005151 – ident: ref11 doi: 10.1109/TNNLS.2018.2885799 – ident: ref38 doi: 10.1109/TNNLS.2020.2979546 – ident: ref28 doi: 10.1109/JSTARS.2021.3063797 – ident: ref31 doi: 10.1109/TGRS.2017.2728186 – ident: ref63 doi: 10.1109/TGRS.2017.2685945
SSID	ssj0062793
Score	2.4110906
Snippet	Polarimetric synthetic aperture radar (PolSAR) terrain segmentation is a fundamental research topic in PolSAR image interpretation. Recently, many studies have...
SourceID	doaj proquest crossref ieee
SourceType	Open Website Aggregation Database Enrichment Source Index Database Publisher
StartPage	3830
SubjectTerms	Air Algorithms Annotations Benchmark data set Complexity theory Datasets Deep learning Image analysis Image color analysis Image processing Image segmentation Machine learning Pixels polarimetric synthetic aperture radar (PolSAR) Radar imaging SAR (radar) Spatial resolution Synthetic aperture radar Task analysis Task complexity Terrain terrain segmentation Training Water bodies
SummonAdditionalLinks	– databaseName: IEEE Electronic Library (IEL) dbid: RIE link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Nb9QwEB21FUhcykdBLBTkA8eaJnYSx9zSQqFSVVWbgnoicvxRIW13q90Uwb9nxvGukEBI3OLItpK88ZuxY78BeFOVuelNVnMSUuGFLxU3iC5XQpiA4b7XUe3zy5k6P6-vrvTFFhxszsJ47-PmM_-WLuO_fLewd7RUdoiuE8en3oZtparxrNaadSuhosAuxiOak2RMUhjKM32IJt5MW5wLCoFTVDRxUlL4zQtFsf6UXeUPSo5-5uTh_z3hI9hN8SRrRgN4DFt-_gTuf4z5en_uwdfmdMovFrO2mfLWX79jDTujvd-8RWw8e28Gw1o_MAxd2aVfUr4ILF_fpBNJc4ZlooyZ_4FNkBfZ2Bk7vUEiWj2FzycfLo8_8ZRSgdsiqwdeCCNdqLTNfWky76QJOqhQuRCMKJ3Tppd5KIyklMTKSFtUlIqj7DNd95kR8hnszBdz_xxYbSonnLalRQ_XO6FJ1rjWauxRygmI9SfubNIbp7QXsy7OOzLdjbh0hEuXcJnAwabR7Si38e_qR4TdpippZccbCEqXhl5nc1f0ymZ9qGSBBKYd8ju9lexra7WawB4BuekkYTiB_bUldGlcrzoMZxQyIkZRL_7e6iU8oAccF2n2YWdY3vlXcM9-H76tlq-jyf4CRlHkrg priority: 102 providerName: IEEE
Title	AIR-PolSAR-Seg: A Large-Scale Data Set for Terrain Segmentation in Complex-Scene PolSAR Images
URI	https://ieeexplore.ieee.org/document/9765389 https://www.proquest.com/docview/2667011190 https://doaj.org/article/c1d4b7c0bf6348329d11127833b8cc97
Volume	15
WOSCitedRecordID	wos000798194300010&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/IET Electronic Library 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/eLvHCXMwrV1LT9wwELYqRCUuiJaibqHIhx5r4dhJHHMLrxYJIbShFadajh8IaVmq3YDg3zN-7AqpEr1wdBQ7yczn8Uxkfx9C3-qq0L2mDQlEKqR0lSAavEsEY9pDuu9kZPv8fSbOz5urK3nxQuor7AlL9MDJcHumsGUvDO19zUuAn7QwO4M8BO8bY2Q8R06FXBRTKQbXDGCXOYYKKvcA5O24g2qQMShSAeSBS-HFOhTp-rO-yj9BOa40JxtoPaeIuE2v9gG9c9OP6P2PKMH7tIn-tKdjcnE36dox6dz1Pm7xWdjOTTowt8NHetC4cwOGbBRfulmQgID29W0-ZDTF0A5RYOIeoQuEOpwGw6e3EFvmn9Cvk-PLw58kqyQQU9JmICXT3PpamsJVmjrLtZde-Np6r1llrdQ9L3ypeVAZFpqbsg7mq3oqm55qxrfQyvRu6j4j3OjaMitNZWDR6i2Tgam4kSKNyPkIsYXNlMkU4kHJYqJiKUGlSoZWwdAqG3qEvi87_U0MGq_ffhCcsbw10F_HCwAKlUGh_geKEdoMrlwOAjkXBHY5QjsL16o8VecKMhQBQQ4Soy9v8ehttBY-J_2l2UErw-zefUWr5mG4mc92I0p34ynDZ4oP5ek
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3Pb9MwFH4aA8Qu48eGKBvgA8eZJXYSx9yywVhFqaamoJ2wHNuZkLp2ajME_z3PjlshgZC4xZFtJfmev_fs2N8DeF3kqW50UlIvpEIzlwuqEV0qGNMthvtOBrXPLyMxHpeXl_JiC442Z2Gcc2HzmXvjL8O_fLswt36p7BhdJ45PeQfu5lnGkv601pp3CyaCxC5GJJJ60ZioMZQm8hiNvJrUOBtkDCepaOReS-E3PxTk-mN-lT9IOXias4f_94yPYDdGlKTqTeAxbLn5E7j_IWTs_bkHX6vhhF4sZnU1obW7eksqMvK7v2mN6DjyTnea1K4jGLySqVv6jBFYvrqOZ5LmBMueNGbuBzZBZiR9Z2R4jVS02ofPZ--np-c0JlWgJkvKjmZMc9sW0qQu14mzXLeyFW1h21az3FqpG562meY-KbHQ3GSFT8aRN4ksm0Qz_hS254u5ewak1IVlVprcoI9rLJNe2LiUou-R8wGw9SdWJiqO-8QXMxVmHolUPS7K46IiLgM42jS66QU3_l39xGO3qerVssMNBEXFwadMarNGmKRpC54hhUmLDO_fijelMVIMYM8DuekkYjiAw7UlqDiyVwoDGoGciHHU87-3egUPzqefRmo0HH88gB3_sP2SzSFsd8tb9wLume_dt9XyZTDfX1Os5_U
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=AIR-PolSAR-Seg%3A+A+Large-Scale+Data+Set+for+Terrain+Segmentation+in+Complex-Scene+PolSAR+Images&rft.jtitle=IEEE+journal+of+selected+topics+in+applied+earth+observations+and+remote+sensing&rft.au=Wang%2C+Zhirui&rft.au=Zeng%2C+Xuan&rft.au=Yan%2C+Zhiyuan&rft.au=Kang%2C+Jian&rft.date=2022&rft.issn=1939-1404&rft.eissn=2151-1535&rft.volume=15&rft.spage=3830&rft.epage=3841&rft_id=info:doi/10.1109%2FJSTARS.2022.3170326&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_JSTARS_2022_3170326
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