A Novel Evolutionary Swarm Fuzzy Clustering Approach for Hyperspectral Imagery
In land cover assessment, classes often gradually change from one to another. Therefore, it is difficult to allocate sharp boundaries between different classes of interest. To overcome this issue and model such conditions, fuzzy techniques that resemble human reasoning have been proposed as alternat...
Uloženo v:
| Vydáno v: | IEEE journal of selected topics in applied earth observations and remote sensing Ročník 8; číslo 6; s. 2447 - 2456 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
01.06.2015
|
| Témata: | |
| ISSN: | 1939-1404, 2151-1535 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | In land cover assessment, classes often gradually change from one to another. Therefore, it is difficult to allocate sharp boundaries between different classes of interest. To overcome this issue and model such conditions, fuzzy techniques that resemble human reasoning have been proposed as alternatives. Fuzzy C-means is the most common fuzzy clustering technique, but its concept is based on a local search mechanism and its convergence rate is rather slow, especially considering high-dimensional problems (e.g., in processing of hyperspectral images). Here, in order to address those shortcomings of hard approaches, a new approach is proposed, i.e., fuzzy C-means which is optimized by fractional order Darwinian particle swarm optimization. In addition, to speed up the clustering process, the histogram of image intensities is used during the clustering process instead of the raw image data. Furthermore, the proposed clustering approach is combined with support vector machine classification to accurately classify hyperspectral images. The new classification framework is applied on two well-known hyperspectral data sets; Indian Pines and Salinas. Experimental results confirm that the proposed swarm-based clustering approach can group hyperspectral images accurately in a time-efficient manner compared to other existing clustering techniques. |
|---|---|
| AbstractList | In land cover assessment, classes often gradually change from one to another. Therefore, it is difficult to allocate sharp boundaries between different classes of interest. To overcome this issue and model such conditions, fuzzy techniques that resemble human reasoning have been proposed as alternatives. Fuzzy C-means is the most common fuzzy clustering technique, but its concept is based on a local search mechanism and its convergence rate is rather slow, especially considering high-dimensional problems (e.g., in processing of hyperspectral images). Here, in order to address those shortcomings of hard approaches, a new approach is proposed, i.e., fuzzy C-means which is optimized by fractional order Darwinian particle swarm optimization. In addition, to speed up the clustering process, the histogram of image intensities is used during the clustering process instead of the raw image data. Furthermore, the proposed clustering approach is combined with support vector machine classification to accurately classify hyperspectral images. The new classification framework is applied on two well-known hyperspectral data sets; Indian Pines and Salinas. Experimental results confirm that the proposed swarm-based clustering approach can group hyperspectral images accurately in a time-efficient manner compared to other existing clustering techniques. |
| Author | Benediktsson, Jon Atli Ghamisi, Pedram Couceiro, Micael S. Ali, Abder-Rahman |
| Author_xml | – sequence: 1 givenname: Pedram surname: Ghamisi fullname: Ghamisi, Pedram organization: Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland – sequence: 2 givenname: Abder-Rahman surname: Ali fullname: Ali, Abder-Rahman email: abder-rahman.a.ali@ieee.org organization: ISIT, Clermont Universite, Universite d’Auvergne, Clermont-Ferrand, France – sequence: 3 givenname: Micael S. surname: Couceiro fullname: Couceiro, Micael S. email: micaelcouceiro@isr.uc.pt organization: Artificial Perception for Intelligent Systems and Robotics (AP4ISR), Institute of Systems and Robotics (ISR), University of Coimbra, Coimbra, Portugal – sequence: 4 givenname: Jon Atli surname: Benediktsson fullname: Benediktsson, Jon Atli email: benedikt@hi.is organization: Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland |
| BookMark | eNqFkNFqwjAUhsNwMHV7Am_yAnU5Sdoml0V0OsTBdNclponrqG1JqqM-_VqUXexmVwcOfP85_zdCg7IqDUITIFMAIp9ft7vkfTulBMIpZVIIFt6hIYUQAghZOEBDkEwGwAl_QCPvvwiJaCzZEG0SvKnOpsDzc1WcmrwqlWvx9lu5I16cLpcWz4qTb4zLywNO6tpVSn9iWzm8bGvjfG1041SBV0d1MK59RPdWFd483eYYfSzmu9kyWL-9rGbJOtA8DptAKplBaLimVjKhRCajaG8F1XtuswwIAGRWRIIZKmKr90KRbiuMEFySjBI2Ruyaq13lvTM2rV1-7F5PgaS9kvSqJO2VpDclHSX_UDpvVF-665AX_7CTK5sbY36vxSRiknD2A4vvcuI |
| CODEN | IJSTHZ |
| CitedBy_id | crossref_primary_10_1109_JBHI_2018_2803020 crossref_primary_10_1109_TMM_2024_3394975 crossref_primary_10_1016_j_infrared_2022_104241 crossref_primary_10_1109_ACCESS_2020_3014211 crossref_primary_10_1016_j_ins_2019_02_008 crossref_primary_10_1109_LGRS_2017_2786732 crossref_primary_10_3233_JIFS_230511 crossref_primary_10_1080_02564602_2020_1740615 crossref_primary_10_1080_01431161_2019_1601284 crossref_primary_10_1109_TCYB_2018_2856269 crossref_primary_10_1016_j_jocs_2018_01_003 crossref_primary_10_3390_rs13112125 crossref_primary_10_1007_s11227_021_04278_2 crossref_primary_10_1080_00207179_2019_1613561 crossref_primary_10_1109_JSTARS_2017_2788426 crossref_primary_10_1109_MGRS_2016_2616418 crossref_primary_10_1109_ACCESS_2023_3283274 crossref_primary_10_1109_MGRS_2017_2762087 crossref_primary_10_1016_j_engappai_2019_04_007 crossref_primary_10_1080_10106049_2021_1945149 crossref_primary_10_1080_10106049_2021_1944453 crossref_primary_10_1007_s12517_017_3196_5 |
| Cites_doi | 10.1016/j.eswa.2010.07.112 10.1101/gr.9.11.1093 10.1109/LGRS.2013.2257675 10.1016/j.ipl.2006.10.005 10.1109/TGRS.2013.2292544 10.1109/TGRS.2013.2260552 10.1016/0098-3004(84)90020-7 10.1093/mnras/202.3.615 10.1007/978-3-642-14880-4_60 10.1109/TGRS.2014.2367010 10.1117/12.2027641 10.1109/TIT.1982.1056489 10.1016/j.eswa.2012.04.078 10.1109/WCICA.2006.1713041 10.1109/IGARSS.2012.6351718 10.1007/978-3-540-89876-4_7 10.1145/1055558.1055581 10.4018/978-1-4666-6030-4.ch001 10.1109/JSTARS.2014.2298876 10.1109/JPROC.2012.2197589 10.1109/TGRS.2013.2263282 10.1016/j.patrec.2009.09.011 10.1109/LGRS.2014.2337320 10.1109/TEVC.2007.896686 10.1145/1137856.1137880 10.1063/1.4765496 |
| ContentType | Journal Article |
| DBID | 97E RIA RIE AAYXX CITATION |
| DOI | 10.1109/JSTARS.2015.2398835 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Geology |
| EISSN | 2151-1535 |
| EndPage | 2456 |
| ExternalDocumentID | 10_1109_JSTARS_2015_2398835 7063904 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: Icelandic Research Fund for Graduate Students |
| 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 |
| ID | FETCH-LOGICAL-c475t-9a9d15e4c2f938a8d966bf82cb4fdd10111df8683e287fcb8a0d108e88490d203 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 29 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000359264000012&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 | Sat Nov 29 06:38:23 EST 2025 Tue Nov 18 22:23:55 EST 2025 Wed Aug 27 02:22:17 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 6 |
| Keywords | fuzzy C-means (FCM) fractional order Darwinian particle swarm optimization (FODPSO) support vector machine (SVM) classifier Clustering hyperspectral image analysis |
| 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-c475t-9a9d15e4c2f938a8d966bf82cb4fdd10111df8683e287fcb8a0d108e88490d203 |
| PageCount | 10 |
| ParticipantIDs | crossref_primary_10_1109_JSTARS_2015_2398835 crossref_citationtrail_10_1109_JSTARS_2015_2398835 ieee_primary_7063904 |
| PublicationCentury | 2000 |
| PublicationDate | 2015-06-01 |
| PublicationDateYYYYMMDD | 2015-06-01 |
| PublicationDate_xml | – month: 06 year: 2015 text: 2015-06-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationTitle | IEEE journal of selected topics in applied earth observations and remote sensing |
| PublicationTitleAbbrev | JSTARS |
| PublicationYear | 2015 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| References | ref35 ref12 ref34 ref15 ref37 ref14 ref30 kennedy (ref5) 0; 34 ref11 ref33 ref10 ref32 gibou (ref17) 0 ray (ref19) 0 tou (ref20) 1974 ref16 ref38 ref18 xian-cheng (ref25) 0 bezdek (ref3) 1973 pallant (ref36) 2011 ref24 ref26 duda (ref1) 2001 ref22 ref21 ref28 ref27 tillett (ref31) 0 dutta (ref2) 2009 ref29 hastie (ref13) 2009 ref8 ref7 ref9 ref4 couceiro (ref6) 2007; 102 bezdek (ref23) 1981; 10 |
| References_xml | – ident: ref26 doi: 10.1016/j.eswa.2010.07.112 – year: 2001 ident: ref1 publication-title: Pattern Classification – ident: ref18 doi: 10.1101/gr.9.11.1093 – ident: ref9 doi: 10.1109/LGRS.2013.2257675 – year: 1973 ident: ref3 publication-title: Fuzzy mathematics in pattern classification – year: 2009 ident: ref13 publication-title: The Elements of Statistical Learning Data Mining Inference and Prediction – ident: ref34 doi: 10.1016/j.ipl.2006.10.005 – year: 1974 ident: ref20 publication-title: Pattern Recognition Principles – ident: ref12 doi: 10.1109/TGRS.2013.2292544 – start-page: 785 year: 0 ident: ref19 article-title: Determination of number of clusters in k-means clustering and application in colour image segmentation publication-title: Proc 4th Int Conf Adv Pattern Recog Digital Tech – ident: ref8 doi: 10.1109/TGRS.2013.2260552 – volume: 10 start-page: 191 year: 1981 ident: ref23 article-title: FCM: The fuzzy c-means clustering algorithm publication-title: Comput Geosci doi: 10.1016/0098-3004(84)90020-7 – ident: ref35 doi: 10.1093/mnras/202.3.615 – ident: ref22 doi: 10.1007/978-3-642-14880-4_60 – ident: ref29 doi: 10.1109/TGRS.2014.2367010 – ident: ref10 doi: 10.1117/12.2027641 – volume: 102 start-page: 8 year: 2007 ident: ref6 article-title: Introducing the fractional order darwinian PSO publication-title: Signal Image Video Process – ident: ref15 doi: 10.1109/TIT.1982.1056489 – ident: ref7 doi: 10.1016/j.eswa.2012.04.078 – ident: ref4 doi: 10.1109/WCICA.2006.1713041 – start-page: 281 year: 0 ident: ref17 article-title: A fast hybrid k-means level set algorithm for segmentation publication-title: Proc 4th Annu Hawaii Int Conf Stat Math – ident: ref32 doi: 10.1109/IGARSS.2012.6351718 – ident: ref24 doi: 10.1007/978-3-540-89876-4_7 – start-page: 611 year: 0 ident: ref25 article-title: Image segmentation based on modified particle swarm optimization and fuzzy c-means clustering algorithm publication-title: Proc 2nd Int Conf Intell Comput Technol Autom – ident: ref16 doi: 10.1145/1055558.1055581 – ident: ref30 doi: 10.4018/978-1-4666-6030-4.ch001 – ident: ref11 doi: 10.1109/JSTARS.2014.2298876 – start-page: 1474 year: 0 ident: ref31 article-title: Darwinian particle swarm optimization publication-title: Proc 2nd Indian Int Conf Artif Intell – ident: ref33 doi: 10.1109/JPROC.2012.2197589 – year: 2009 ident: ref2 article-title: Fuzzy c-means classification of multispectral data incorporating spatial contextual information by using Markov random field – ident: ref38 doi: 10.1109/TGRS.2013.2263282 – year: 2011 ident: ref36 publication-title: SPSS Survival Manual – ident: ref21 doi: 10.1016/j.patrec.2009.09.011 – ident: ref28 doi: 10.1109/LGRS.2014.2337320 – ident: ref27 doi: 10.1109/TEVC.2007.896686 – volume: 34 start-page: 39 year: 0 ident: ref5 article-title: A new optimizer using particle swarm theory publication-title: Proc IEEE Int Symp Micro Mach Human Sci – ident: ref14 doi: 10.1145/1137856.1137880 – ident: ref37 doi: 10.1063/1.4765496 |
| SSID | ssj0062793 |
| Score | 2.21514 |
| Snippet | In land cover assessment, classes often gradually change from one to another. Therefore, it is difficult to allocate sharp boundaries between different classes... |
| SourceID | crossref ieee |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 2447 |
| SubjectTerms | Accuracy Clustering Clustering algorithms Clustering methods fractional order Darwinian particle swarm optimization (FODPSO) fuzzy C-means (FCM) hyperspectral image analysis Hyperspectral imaging support vector machine (SVM) classifier Support vector machines Training |
| Title | A Novel Evolutionary Swarm Fuzzy Clustering Approach for Hyperspectral Imagery |
| URI | https://ieeexplore.ieee.org/document/7063904 |
| Volume | 8 |
| WOSCitedRecordID | wos000359264000012&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: 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/eLvHCXMwlV1LS8NAEF5qUfDiq4r1xR48Nm0em2T2GEprBQliVXoLm90NCH1IbCrpr3d3k1YPInhbltkQvgyZmWTm-xC6DQQQh0Nq2anQEmacWCmExBI0UOWzAMmMHNDrQxjHMJnQxwbqbGdhpJSm-Ux29dL8yxcLXuhPZb1Qx1NN_rkThkE1q7V56wZuaAh2VT5CLU0ZUzMMOTbtKRePnsa6jcvvaro7MNpu31Hoh6yKiSrDw__dzxE6qLNHHFWP-xg15PwE7d0Zdd6yheIIx4uVnOLBqvYolpd4_MnyGR4W63WJ-9NCMyOoeIWjmk0cq7QVj1Q5Wk1d5ur69zPNbFGeopfh4Lk_smrBBIuT0F9alFHh-JJwN6MeMFCAB2kGLk9JJoSjZeVFBgF4UtVJGU-B2WoXJAChtnBt7ww154u5PEc4UBYe4So58nziSMF8ptYAWapiHufQRu4GwITXbOJa1GKamKrCpkmFeqJRT2rU26izPfRekWn8bd7SmG9Na7gvft--RPv6cNXGdYWay7yQ12iXr5ZvH_mNcZYvl9-7pA |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8NAEF6KD_Tiq4r1uQePTZvHJpk9ltLaYg1iq_QWkt0NCH1IbCrpr3d3k1YPInhblskSvgyZmWTm-xC68zgQi0FsmDFXEmaMGDH4xODUk-UzBxFpOaDXgR8EMB7Tpwqqb2ZhhBC6-Uw01FL_y-dzlqlPZU1fxVNF_rntEmKbxbTW-r3r2b6m2JUZCTUUaUzJMWSZtCmdvPU8VI1cbkMR3oFWd_uOQz-EVXRc6R7-746O0EGZP-JW8cCPUUXMTtDuvdbnzasoaOFgvhQT3FmWPhWlOR5-RukUd7PVKsftSaa4EWTEwq2STxzLxBX3ZEFazF2m8vz-VHFb5KfopdsZtXtGKZlgMOK7C4NGlFuuIMxOqAMRSMi9OAGbxSTh3FLC8jwBDxwhK6WExRCZchcEAKEmt03nDG3N5jNxjrAnLRzCZHrkuMQSPHIjuQZIYhn1GIMastcAhqzkE1eyFpNQ1xUmDQvUQ4V6WKJeQ_XNRe8Fncbf5lWF-ca0hPvi9-1btNcbPQ7CQT94uET76qCiqesKbS3STFyjHbZcvH2kN9pxvgCIeL7r |
| 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=A+Novel+Evolutionary+Swarm+Fuzzy+Clustering+Approach+for+Hyperspectral+Imagery&rft.jtitle=IEEE+journal+of+selected+topics+in+applied+earth+observations+and+remote+sensing&rft.au=Ghamisi%2C+Pedram&rft.au=Ali%2C+Abder-Rahman&rft.au=Couceiro%2C+Micael+S.&rft.au=Benediktsson%2C+J%C3%B3n+Atli&rft.date=2015-06-01&rft.issn=1939-1404&rft.eissn=2151-1535&rft.volume=8&rft.issue=6&rft.spage=2447&rft.epage=2456&rft_id=info:doi/10.1109%2FJSTARS.2015.2398835&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_JSTARS_2015_2398835 |
| 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 |