AIS Data Aided Rayleigh CFAR Ship Detection Algorithm of Multiple-Target Environment in SAR Images

This article proposes an automatic identification system (AIS) data aided Rayleigh constant false alarm rate (AIS-RCFAR) ship detection algorithm of multiple-target environment in synthetic aperture radar (SAR) images. This method aims to improve the detection performance in complex environment with...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on aerospace and electronic systems Jg. 58; H. 2; S. 1266 - 1282
Hauptverfasser:	Ai, Jiaqiu, Pei, Zhilin, Yao, Baidong, Wang, Zhaocheng, Xing, Mengdao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.04.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptive-depth based clutter trimming Algorithms Artificial intelligence Automatic Identification System (AIS) information Closed-form solution Clutter Constant false alarm rate Constant false alarm rate (CFAR) Detectors False alarms Marine vehicles Maximum likelihood estimators Outliers (statistics) Parameter estimation Pixels Probability density functions Radar imaging Radar polarimetry Rayleigh distribution SAR ship detection Synthetic aperture radar Target detection Trimming
ISSN:	0018-9251, 1557-9603
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	This article proposes an automatic identification system (AIS) data aided Rayleigh constant false alarm rate (AIS-RCFAR) ship detection algorithm of multiple-target environment in synthetic aperture radar (SAR) images. This method aims to improve the detection performance in complex environment with the aid of AIS data. Traditional CFAR detectors generally use all the samples in the local background window for parameter estimation. However, in multiple-target environment, clutter edges and transition areas, due to the interference of the high-intensity outliers, such as target pixels, ghosts, and other interfering pixels, the parameters are often overestimated, causing degradation of the detection performance. Aiming at solving this problem, AIS-RCFAR designs an adaptive-threshold based clutter trimming method with an adaptive-trimming-depth aided by AIS data to effectively eliminate the high-intensity outliers in the local background window while greatly sustaining the real sea clutter samples. Maximum-likelihood-estimator with a closed-form solution is proposed to precisely estimate the parameters using the adaptively-trimmed clutter samples, the probability density function of the sea clutter following Rayleigh distribution can be accurately modeled. AIS-RCFAR greatly enhances the detection rate in both homogeneous and nonhomogeneous multiple-target environment, it also achieves a very low false alarm rate. In addition, the whole procedure of AIS-RCFAR is simple and efficient. Simulated data and real SAR images with corresponding matched AIS data are used for experiments to validate the superiority and feasibility of AIS-RCFAR.
AbstractList	This article proposes an automatic identification system (AIS) data aided Rayleigh constant false alarm rate (AIS-RCFAR) ship detection algorithm of multiple-target environment in synthetic aperture radar (SAR) images. This method aims to improve the detection performance in complex environment with the aid of AIS data. Traditional CFAR detectors generally use all the samples in the local background window for parameter estimation. However, in multiple-target environment, clutter edges and transition areas, due to the interference of the high-intensity outliers, such as target pixels, ghosts, and other interfering pixels, the parameters are often overestimated, causing degradation of the detection performance. Aiming at solving this problem, AIS-RCFAR designs an adaptive-threshold based clutter trimming method with an adaptive-trimming-depth aided by AIS data to effectively eliminate the high-intensity outliers in the local background window while greatly sustaining the real sea clutter samples. Maximum-likelihood-estimator with a closed-form solution is proposed to precisely estimate the parameters using the adaptively-trimmed clutter samples, the probability density function of the sea clutter following Rayleigh distribution can be accurately modeled. AIS-RCFAR greatly enhances the detection rate in both homogeneous and nonhomogeneous multiple-target environment, it also achieves a very low false alarm rate. In addition, the whole procedure of AIS-RCFAR is simple and efficient. Simulated data and real SAR images with corresponding matched AIS data are used for experiments to validate the superiority and feasibility of AIS-RCFAR.
Author	Yao, Baidong Ai, Jiaqiu Pei, Zhilin Xing, Mengdao Wang, Zhaocheng
Author_xml	– sequence: 1 givenname: Jiaqiu orcidid: 0000-0001-7923-0172 surname: Ai fullname: Ai, Jiaqiu email: aijiaqiu1985@hfut.edu.cn organization: Hefei University of Technology, Hefei, China – sequence: 2 givenname: Zhilin orcidid: 0000-0003-4143-529X surname: Pei fullname: Pei, Zhilin email: zhilinpei@mail.hfut.edu.cn organization: Hefei University of Technology, Hefei, China – sequence: 3 givenname: Baidong surname: Yao fullname: Yao, Baidong email: yao1984@mail.ustc.edu.cn organization: China Electronics Technology Corporation, Beijing, China – sequence: 4 givenname: Zhaocheng orcidid: 0000-0003-4890-2540 surname: Wang fullname: Wang, Zhaocheng email: zcwang@hebut.edu.cn organization: Hebei University of Technology, Tianjin, China – sequence: 5 givenname: Mengdao orcidid: 0000-0002-4084-0915 surname: Xing fullname: Xing, Mengdao email: xmd@xidian.edu.cn organization: Xidian University, Xian, China
BookMark	eNp9kE1rwkAQhpdioWr7A0ovCz3H7uzmY_cY_GgFS0HtOYxxoisxsZu14L9vROmhh15mGHifmeHpsU5VV8TYI4gBgDAvy3S8GEghYaAAQIfmhnUhipLAxEJ1WFcI0IGREdyxXtPs2jHUoeqyVTpd8BF65Kld05rP8VSS3Wz5cJLO-WJrD3xEnnJv64qn5aZ21m_3vC74-7H09lBSsES3Ic_H1bd1dbWnynNb8UWLT_e4oeae3RZYNvRw7X32ORkvh2_B7ON1OkxnQS6l8kFbtF4XkRFoxKoICw3UPp0IgXGuAUmizGMdFzKMMWkHvSaNAuUKVIwQqj57vuw9uPrrSI3PdvXRVe3JTMahSYySGtoUXFK5q5vGUZEdnN2jO2UgsrPK7KwyO6vMripbJvnD5NbjWYl3aMt_yacLaYno95KJVCSlUT9_LIDb
CODEN	IEARAX
CitedBy_id	crossref_primary_10_1080_01431161_2023_2182652 crossref_primary_10_1109_JSTARS_2025_3600243 crossref_primary_10_1109_TGRS_2024_3449894 crossref_primary_10_1109_LGRS_2025_3527763 crossref_primary_10_1080_17538947_2025_2507842 crossref_primary_10_1155_2023_1240426 crossref_primary_10_1109_TAES_2024_3480052 crossref_primary_10_1109_TGRS_2024_3521971 crossref_primary_10_3390_rs15133322 crossref_primary_10_1109_JSTARS_2025_3533140 crossref_primary_10_1109_TAES_2023_3274101 crossref_primary_10_1109_LSP_2024_3396657 crossref_primary_10_1109_TGRS_2022_3227938 crossref_primary_10_1109_TGRS_2025_3534234 crossref_primary_10_1109_TGRS_2022_3217035 crossref_primary_10_1109_MGRS_2024_3404506 crossref_primary_10_1109_JSTARS_2024_3354271 crossref_primary_10_3390_rs16142551 crossref_primary_10_1007_s13131_021_1980_2 crossref_primary_10_1109_TAES_2022_3198034 crossref_primary_10_1109_LGRS_2024_3450293 crossref_primary_10_1109_TGRS_2022_3188151 crossref_primary_10_1109_JSTARS_2024_3469209 crossref_primary_10_1109_JSTARS_2025_3592850 crossref_primary_10_3390_rs15184480
Cites_doi	10.1109/APSAR.2009.5374239 10.1109/ACSSC.1991.186564 10.1109/TAES.1973.309705 10.1117/12.262719 10.1109/IGARSS.2001.977077 10.1109/TITS.2019.2911692 10.1109/TAES.1982.309210 10.1109/TAES.1977.308466 10.1109/TGRS.2020.2979252 10.1109/TAES.1980.308885 10.1109/TGRS.2014.2363356 10.1109/RADAR.2013.6652006 10.1109/LGRS.2012.2224317 10.1117/12.864820 10.1109/TGRS.2013.2282820 10.1109/JOE.2017.2767106 10.1080/713821561 10.1109/7.570713 10.1109/TGRS.2010.2071879 10.3390/rs1030375 10.1109/TAES.1978.308625 10.1109/TAES.2021.3050654 10.1117/1.JRS.7.071599 10.1109/7.256288 10.1109/7.869503 10.1109/LGRS.2010.2098434 10.1109/JSTSP.2011.2138675 10.1017/S0373463313000702 10.3390/e15062218 10.1080/07038992.2001.10854896 10.1109/JOE.2017.2768198 10.1109/LGRS.2010.2048697 10.1109/TIP.2004.823825 10.1109/IGARSS.2011.6048956 10.1109/LGRS.2018.2838263 10.1109/ICMLC.2005.1527194 10.1109/7.78295 10.1109/LGRS.2010.2090492 10.1109/LGRS.2010.2041322 10.1109/TGRS.2015.2451311 10.3390/s17071578 10.1109/IGARSS.2008.4778892 10.1109/36.581981
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 RIA RIE AAYXX CITATION 7SP 7TB 8FD FR3 H8D L7M
DOI	10.1109/TAES.2021.3111849
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef Electronics & Communications Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database Engineering Research Database Aerospace Database Advanced Technologies Database with Aerospace
DatabaseTitle	CrossRef Aerospace Database Engineering Research Database Technology Research Database Mechanical & Transportation Engineering Abstracts Advanced Technologies Database with Aerospace Electronics & Communications Abstracts
DatabaseTitleList	Aerospace Database
Database_xml	– sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1557-9603
EndPage	1282
ExternalDocumentID	10_1109_TAES_2021_3111849 9535229
Genre	orig-research
GrantInformation_xml	– fundername: China Postdoctoral Science Foundation grantid: 2020T130165 funderid: 10.13039/501100002858 – fundername: Fundamental Research Funds for the Central Universities grantid: JZ2020HGTB0012; PA2021AKSK0113 funderid: 10.13039/501100012226 – fundername: National Natural Science Foundation of China grantid: 62071164; 61701157; 62001155 funderid: 10.13039/501100001809
GroupedDBID	-~X 0R~ 29I 4.4 41~ 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACIWK ACNCT AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ASUFR ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P H~9 IAAWW IBMZZ ICLAB IFIPE IFJZH IPLJI JAVBF LAI M43 OCL P2P RIA RIE RNS TN5 VH1 AAYXX CITATION 7SP 7TB 8FD FR3 H8D L7M
ID	FETCH-LOGICAL-c223t-22388df590a90bf4f81e018700a6c81ae2a2c686f246a72a28de8a0a2b136a143
IEDL.DBID	RIE
ISICitedReferencesCount	32
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000782444700040&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0018-9251
IngestDate	Mon Jun 30 10:12:58 EDT 2025 Sat Nov 29 01:51:32 EST 2025 Tue Nov 18 21:20:27 EST 2025 Wed Aug 27 02:40:46 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	2
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c223t-22388df590a90bf4f81e018700a6c81ae2a2c686f246a72a28de8a0a2b136a143
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0003-4890-2540 0000-0001-7923-0172 0000-0002-4084-0915 0000-0003-4143-529X
PQID	2649793281
PQPubID	85477
PageCount	17
ParticipantIDs	crossref_primary_10_1109_TAES_2021_3111849 crossref_citationtrail_10_1109_TAES_2021_3111849 proquest_journals_2649793281 ieee_primary_9535229
PublicationCentury	2000
PublicationDate	2022-04-01
PublicationDateYYYYMMDD	2022-04-01
PublicationDate_xml	– month: 04 year: 2022 text: 2022-04-01 day: 01
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	IEEE transactions on aerospace and electronic systems
PublicationTitleAbbrev	T-AES
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	ref35 ref12 Novak (ref13) 1993; 6 ref34 ref15 ref37 ref36 ref31 ref30 ref11 ref33 ref10 ref32 ref2 ref1 ref17 ref39 ref16 ref38 ref19 ref18 ref24 ref23 ref45 ref26 ref25 ref20 ref42 ref41 ref22 ref44 ref21 ref43 ref28 ref27 Crisp (ref14) 2004; 35 ref29 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40
References_xml	– volume: 6 start-page: 11 issue: 1 year: 1993 ident: ref13 article-title: Performance of a high-resolution polarimetric SAR automatic target recognition system publication-title: Linc Lab. J. – ident: ref35 doi: 10.1109/APSAR.2009.5374239 – ident: ref32 doi: 10.1109/ACSSC.1991.186564 – ident: ref17 doi: 10.1109/TAES.1973.309705 – ident: ref27 doi: 10.1117/12.262719 – ident: ref12 doi: 10.1109/IGARSS.2001.977077 – ident: ref39 doi: 10.1109/TITS.2019.2911692 – ident: ref30 doi: 10.1109/TAES.1982.309210 – ident: ref33 doi: 10.1109/TAES.1977.308466 – ident: ref42 doi: 10.1109/TGRS.2020.2979252 – ident: ref31 doi: 10.1109/TAES.1980.308885 – ident: ref26 doi: 10.1109/TGRS.2014.2363356 – ident: ref4 doi: 10.1109/RADAR.2013.6652006 – ident: ref25 doi: 10.1109/LGRS.2012.2224317 – ident: ref28 doi: 10.1117/12.864820 – ident: ref37 doi: 10.1109/TGRS.2013.2282820 – ident: ref3 doi: 10.1109/JOE.2017.2767106 – ident: ref19 doi: 10.1080/713821561 – ident: ref10 doi: 10.1109/7.570713 – ident: ref1 doi: 10.1109/TGRS.2010.2071879 – ident: ref2 doi: 10.3390/rs1030375 – ident: ref29 doi: 10.1109/TAES.1978.308625 – ident: ref40 doi: 10.1109/TAES.2021.3050654 – ident: ref43 doi: 10.1117/1.JRS.7.071599 – ident: ref22 doi: 10.1109/7.256288 – ident: ref34 doi: 10.1109/7.869503 – ident: ref36 doi: 10.1109/LGRS.2010.2098434 – ident: ref24 doi: 10.1109/JSTSP.2011.2138675 – ident: ref8 doi: 10.1017/S0373463313000702 – ident: ref9 doi: 10.3390/e15062218 – ident: ref11 doi: 10.1080/07038992.2001.10854896 – ident: ref16 doi: 10.1109/JOE.2017.2768198 – ident: ref18 doi: 10.1109/LGRS.2010.2048697 – ident: ref45 doi: 10.1109/TIP.2004.823825 – ident: ref38 doi: 10.1109/IGARSS.2011.6048956 – ident: ref41 doi: 10.1109/LGRS.2018.2838263 – ident: ref23 doi: 10.1109/ICMLC.2005.1527194 – ident: ref21 doi: 10.1109/7.78295 – ident: ref44 doi: 10.1109/LGRS.2010.2090492 – ident: ref7 doi: 10.1109/LGRS.2010.2041322 – volume: 35 start-page: 2165 issue: 42 year: 2004 ident: ref14 article-title: The state-of-the-art in ship detection in synthetic aperture radar imagery publication-title: Org. Lett. – ident: ref15 doi: 10.1109/TGRS.2015.2451311 – ident: ref5 doi: 10.3390/s17071578 – ident: ref6 doi: 10.1109/IGARSS.2008.4778892 – ident: ref20 doi: 10.1109/36.581981
SSID	ssj0014843
Score	2.5035908
Snippet	This article proposes an automatic identification system (AIS) data aided Rayleigh constant false alarm rate (AIS-RCFAR) ship detection algorithm of...
SourceID	proquest crossref ieee
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	1266
SubjectTerms	Adaptive-depth based clutter trimming Algorithms Artificial intelligence Automatic Identification System (AIS) information Closed-form solution Clutter Constant false alarm rate Constant false alarm rate (CFAR) Detectors False alarms Marine vehicles Maximum likelihood estimators Outliers (statistics) Parameter estimation Pixels Probability density functions Radar imaging Radar polarimetry Rayleigh distribution SAR ship detection Synthetic aperture radar Target detection Trimming
Title	AIS Data Aided Rayleigh CFAR Ship Detection Algorithm of Multiple-Target Environment in SAR Images
URI	https://ieeexplore.ieee.org/document/9535229 https://www.proquest.com/docview/2649793281
Volume	58
WOSCitedRecordID	wos000782444700040&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: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1557-9603 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014843 issn: 0018-9251 databaseCode: RIE dateStart: 19650101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fS8MwED7m8EEf_C1Op-TBJ7Euybo2eSxzw4EOcVP2VtI01YLrxtb595tk2RQUwZfSwh2Ufs3lLvfdHcBl0kyZ6evkyVaiA5REEE9kkntESCWbmR9mlu3-ch_2-2w04o8VuF7XwiilLPlM3Zhbm8tPJ3Jhjsoa3PQioXwDNsIwXNZqrTMGPnMMOaIXsN60XQaTYN4YRp2BjgQp0QGq9qdN28xve5AdqvLDEtvtpbv7vxfbgx3nRqJoifs-VFRxANvfmgseQhL1BuhWlAJFeapS9KRjc3MOitrd6AkN3vIpulWlZWIVKHp_nczy8m2MJhl6cCRDb2hp4qjzVQyH8gINtHpvrO3Q_Aieu51h-85zExU8qd2A0tMXxtKsxbHgOMn8jBFlpvJhLALJiFBUUBmwIKN-IEL9wFLFBBY0Ic1AaNfqGKrFpFAngExGVja5lvepnzLGlQi5ic4obgmhcA3w6hvH0rUbN1Mv3mMbdmAeG1hiA0vsYKnB1Vpluuy18ZfwocFhLeggqEF9BWTsVuM81k4f13aIMnL6u9YZbFFT1mAZOXWolrOFOodN-VHm89mF_dE-Ad0TzaM
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS8MwED_mB6gPfovzMw8-iXVJ1nXJY9GNDecQN8W3kqapK2zd2Kp_v0kWp6AIvpQW7qD011zucr-7A7iIqwkzfZ08WYt1gBIL4olUco8IqWQ19eupZbs_d-rdLnt54Q8luFrUwiilLPlMXZtbm8tPxvLNHJVVuOlFQvkSrNR8n5J5tdYiZ-Azx5EjegnrbdvlMAnmlX7Y6OlYkBIdomqP2jTO_LYL2bEqP2yx3WCaW_97tW3YdI4kCufI70BJ5buw8a294B7EYbuHbkUhUJglKkGPOjo3J6Hophk-ot4gm6BbVVguVo7C4et4mhWDERqn6N7RDL2-JYqjxlc5HMpy1NPq7ZG2RLN9eGo2-jctz81U8KR2BApPXxhL0hrHguM49VNGlJnLh7EIJCNCUUFlwIKU-oGo6weWKCawoDGpBkI7VwewnI9zdQjI5GRllWt5n_oJY1yJOjfxGcU1IRQuA_78xpF0DcfN3IthZAMPzCMDS2RgiRwsZbhcqEzm3Tb-Et4zOCwEHQRlOPkEMnLrcRZpt49rS0QZOfpd6xzWWv37TtRpd--OYZ2aIgfLzzmB5WL6pk5hVb4X2Wx6Zn-6D7zz0Oo
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=AIS+Data+Aided+Rayleigh+CFAR+Ship+Detection+Algorithm+of+Multiple-Target+Environment+in+SAR+Images&rft.jtitle=IEEE+transactions+on+aerospace+and+electronic+systems&rft.au=Ai%2C+Jiaqiu&rft.au=Pei%2C+Zhilin&rft.au=Yao%2C+Baidong&rft.au=Wang%2C+Zhaocheng&rft.date=2022-04-01&rft.pub=The+Institute+of+Electrical+and+Electronics+Engineers%2C+Inc.+%28IEEE%29&rft.issn=0018-9251&rft.eissn=1557-9603&rft.volume=58&rft.issue=2&rft.spage=1266&rft_id=info:doi/10.1109%2FTAES.2021.3111849&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0018-9251&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0018-9251&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0018-9251&client=summon