A Multi-kernel Joint Sparse Graph for SAR Image Segmentation
Recently, more and more attention has been drawn on the study of sparse graph-based classification with respect to pattern recognition and computer vision. Sparse self-representation method features good category distinguishing performance, noise robustness, and data-adaptiveness. In this paper, a m...
Uložené v:
| Vydané v: | IEEE journal of selected topics in applied earth observations and remote sensing Ročník 9; číslo 3; s. 1265 - 1285 |
|---|---|
| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Piscataway
IEEE
01.03.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Predmet: | |
| ISSN: | 1939-1404, 2151-1535 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Recently, more and more attention has been drawn on the study of sparse graph-based classification with respect to pattern recognition and computer vision. Sparse self-representation method features good category distinguishing performance, noise robustness, and data-adaptiveness. In this paper, a multi-kernel joint sparse graph (MKJS-graph) is proposed to segment synthetic aperture radar (SAR) images. At first, an SAR image is over-segmented to many superpixels. Then, a new multikernel sparse representation (MKSR) model is used to express sparsely multiple features of the superpixels in high-dimensional projection space, which can reflect the global similarity of superpixels. Moreover, the local neighborhood spatial correlation of superpixels is combined with the global similarity of that to improve the segmentation performance by formulating the adjacent matrix for MKJS-graph. Integration of the global and local structures of the superpixels provides the MKJS-graph with favorable category distinguishing ability on segmenting SAR images polluted by speckle noise. The simulated, Ku-band, and X-band SAR images are tested through a series of experiments, and the results indicate that the proposed method is more competitive than other state-of-the-art algorithms in SAR image segmentation. |
|---|---|
| AbstractList | Recently, more and more attention has been drawn on the study of sparse graph-based classification with respect to pattern recognition and computer vision. Sparse self-representation method features good category distinguishing performance, noise robustness, and data-adaptiveness. In this paper, a multi-kernel joint sparse graph (MKJS-graph) is proposed to segment synthetic aperture radar (SAR) images. At first, an SAR image is over-segmented to many superpixels. Then, a new multi-kernel sparse representation (MKSR) model is used to express sparsely multiple features of the superpixels in high-dimensional projection space, which can reflect the global similarity of superpixels. Moreover, the local neighborhood spatial correlation of superpixels is combined with the global similarity of that to improve the segmentation performance by formulating the adjacent matrix for MKJS-graph. Integration of the global and local structures of the superpixels provides the MKJS-graph with favorable category distinguishing ability on segmenting SAR images polluted by speckle noise. The simulated, Ku-band, and $X$-band SAR images are tested through a series of experiments, and the results indicate that the proposed method is more competitive than other state-of-the-art algorithms in SAR image segmentation. Recently, more and more attention has been drawn on the study of sparse graph-based classification with respect to pattern recognition and computer vision. Sparse self-representation method features good category distinguishing performance, noise robustness, and data-adaptiveness. In this paper, a multi-kernel joint sparse graph (MKJS-graph) is proposed to segment synthetic aperture radar (SAR) images. At first, an SAR image is over-segmented to many superpixels. Then, a new multikernel sparse representation (MKSR) model is used to express sparsely multiple features of the superpixels in high-dimensional projection space, which can reflect the global similarity of superpixels. Moreover, the local neighborhood spatial correlation of superpixels is combined with the global similarity of that to improve the segmentation performance by formulating the adjacent matrix for MKJS-graph. Integration of the global and local structures of the superpixels provides the MKJS-graph with favorable category distinguishing ability on segmenting SAR images polluted by speckle noise. The simulated, Ku-band, and X-band SAR images are tested through a series of experiments, and the results indicate that the proposed method is more competitive than other state-of-the-art algorithms in SAR image segmentation. Recently, more and more attention has been drawn on the study of sparse graph-based classification with respect to pattern recognition and computer vision. Sparse self-representation method features good category distinguishing performance, noise robustness, and data-adaptiveness. In this paper, a multi-kernel joint sparse graph (MKJS-graph) is proposed to segment synthetic aperture radar (SAR) images. At first, an SAR image is over-segmented to many superpixels. Then, a new multi-kernel sparse representation (MKSR) model is used to express sparsely multiple features of the superpixels in high-dimensional projection space, which can reflect the global similarity of superpixels. Moreover, the local neighborhood spatial correlation of superpixels is combined with the global similarity of that to improve the segmentation performance by formulating the adjacent matrix for MKJS-graph. Integration of the global and local structures of the superpixels provides the MKJS-graph with favorable category distinguishing ability on segmenting SAR images polluted by speckle noise. The simulated, Ku-band, and [Formula Omitted]-band SAR images are tested through a series of experiments, and the results indicate that the proposed method is more competitive than other state-of-the-art algorithms in SAR image segmentation. |
| Author | Yang, Shuyuan Jiao, Licheng Hou, Biao Zhao, Zhiqiang Gu, Jing Liu, Fang |
| Author_xml | – sequence: 1 givenname: Jing surname: Gu fullname: Gu, Jing email: xuer6126@126.com organization: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China – sequence: 2 givenname: Licheng surname: Jiao fullname: Jiao, Licheng organization: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China – sequence: 3 givenname: Shuyuan surname: Yang fullname: Yang, Shuyuan organization: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China – sequence: 4 givenname: Fang surname: Liu fullname: Liu, Fang organization: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China – sequence: 5 givenname: Biao surname: Hou fullname: Hou, Biao organization: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China – sequence: 6 givenname: Zhiqiang surname: Zhao fullname: Zhao, Zhiqiang organization: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, China |
| BookMark | eNqFkLFuwjAQhq2KSgXaJ2CJ1KVLqM-Ok1jqglBLQVSVCJ0t45xpaEioHYa-fYOCOrB0uhv-73T_NyC9qq6QkBHQMQCVj4tsPVllY0ZBjJmgTEq4In0GAkIQXPRIHySXIUQ0uiED73eUxiyRvE-eJsHbsWyK8AtdhWWwqIuqCbKDdh6DmdOHz8DWLsgmq2C-11sMMtzusWp0U9TVLbm2uvR4d55D8vHyvJ6-hsv32Xw6WYaGc9qEeZJQzkUimLF5YixYNDmmsbA6wii3uUwtUNTtylBIo2WUs3gjrNlQnseUD8lDd_fg6u8j-kbtC2-wLHWF9dErSCGmbW8etdH7i-iuPrqq_U5BkiZMpDGcUrJLGVd779AqU3SVGqeLUgFVJ6-q86pOXtXZa8vyC_bgir12P_9Qo44qEPGPSHhMJTD-C3f0hUY |
| CODEN | IJSTHZ |
| CitedBy_id | crossref_primary_10_1007_s11042_021_11416_8 crossref_primary_10_1109_JSTARS_2020_2997666 crossref_primary_10_1109_TSMC_2019_2912206 crossref_primary_10_1109_TGRS_2018_2888891 crossref_primary_10_1016_j_patcog_2016_11_015 crossref_primary_10_1016_j_knosys_2016_12_006 crossref_primary_10_1016_j_patcog_2017_09_016 crossref_primary_10_1109_JSTARS_2017_2716620 crossref_primary_10_1016_j_sigpro_2020_107518 crossref_primary_10_1109_JSTARS_2022_3218983 crossref_primary_10_1109_TGRS_2020_3041281 crossref_primary_10_1109_ACCESS_2018_2889929 crossref_primary_10_3390_rs9101085 crossref_primary_10_1109_JSTARS_2017_2743338 crossref_primary_10_1587_transinf_2017EDL8281 crossref_primary_10_1109_JSTARS_2016_2524918 crossref_primary_10_1109_TGRS_2021_3108585 crossref_primary_10_3233_JIFS_202401 crossref_primary_10_1109_TFUZZ_2017_2686804 crossref_primary_10_1109_JSTARS_2020_2987653 crossref_primary_10_1109_LGRS_2017_2689506 crossref_primary_10_1109_TGRS_2019_2941288 crossref_primary_10_1109_TGRS_2023_3307825 crossref_primary_10_3390_rs10060906 |
| Cites_doi | 10.2528/PIER13041503 10.1109/CVPR.2005.38 10.1126/science.290.5500.2323 10.1137/080716542 10.1109/ICIG.2011.86 10.1109/LGRS.2013.2292820 10.1162/089976603321780317 10.1109/36.35954 10.1109/TGRS.2012.2201730 10.1109/TIP.2014.2322938 10.1109/IGARSS.2012.6351715 10.1016/j.neucom.2014.05.010 10.1080/2150704X.2012.723146 10.1109/TIP.2010.2040763 10.1016/j.neucom.2011.08.018 10.1109/83.951532 10.1109/TSP.2011.2179539 10.1109/TCSVT.2003.818352 10.1109/IGARSS.2009.5417941 10.1109/ICCV.2011.6126528 10.7551/mitpress/6173.003.0016 10.1109/TPAMI.2008.216 10.1109/TIP.2010.2050625 10.1109/TPAMI.2007.70844 10.1109/97.991133 10.1007/s11045-013-0225-8 10.1109/TPAMI.2011.274 10.1109/34.868688 10.1007/978-3-642-33506-8_7 10.1109/TPAMI.2008.15 10.1109/TPAMI.2012.62 10.1109/CVPR.2009.5206547 10.1016/j.ins.2011.02.025 10.1016/j.sigpro.2012.08.024 10.1109/TIT.2005.860430 10.1109/JSTARS.2012.2201249 10.1109/TIP.2009.2038764 10.1109/KAMW.2008.4810503 10.1109/36.673672 10.1016/j.patcog.2013.07.003 10.1109/ICNC.2009.447 10.1109/APSAR.2009.5374114 10.1109/APSAR.2009.5374117 10.1109/TPAMI.2008.79 10.1109/TPAMI.2012.120 10.1007/978-3-642-33506-8_55 10.1109/TSP.2006.881199 10.1109/JSTARS.2012.2230435 10.1109/TGRS.2012.2203604 10.1109/TPAMI.2009.96 10.1109/RADAR.2011.5960546 10.1109/TGRS.2013.2287273 10.1109/TSMC.1973.4309314 10.1109/BIBE.2012.6399658 10.1109/TGRS.2008.918647 10.1109/JSTARS.2013.2255119 10.1016/j.asoc.2011.05.039 10.1109/JPROC.2010.2044470 10.1109/CVPR.2011.5995393 10.1109/TGRS.2007.895416 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2016 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2016 |
| DBID | 97E RIA RIE AAYXX CITATION 7UA 8FD C1K F1W FR3 H8D H96 KR7 L.G L7M |
| DOI | 10.1109/JSTARS.2015.2502991 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present 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 |
| 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 Aerospace Database |
| 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 | 1285 |
| ExternalDocumentID | 4047840781 10_1109_JSTARS_2015_2502991 7360912 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: National Basic Research Program (973 Program) of China grantid: 2013CB329402 – fundername: Program for Cheung Kong Scholars and Innovative Research Team in University grantid: IRT1170 – fundername: Major Research Plan of the National Natural Science Foundation of China grantid: 91438201; 91438103 – fundername: Fund for Foreign Scholars in University Research and Teaching Programs grantid: B07048 – fundername: National Natural Science Foundation of China grantid: 61573267; 61473215; 61472306; 61271302; 61272282; 61202176; 61173090 funderid: 10.13039/501100001809 |
| 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 RIG |
| ID | FETCH-LOGICAL-c330t-d770335752cfd7cf1fecde865fa4e4dfd98f10ea4df2e59ca94d26b5fcb03d603 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 27 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000373054100025&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 Sep 05 08:16:41 EDT 2025 Sun Jul 13 02:56:13 EDT 2025 Tue Nov 18 22:30:42 EST 2025 Sat Nov 29 04:50:52 EST 2025 Wed Aug 27 02:50:26 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| Keywords | sparse representation (SR) synthetic aperture radar (SAR) image segmentation Local spatial relation multi-kernel |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c330t-d770335752cfd7cf1fecde865fa4e4dfd98f10ea4df2e59ca94d26b5fcb03d603 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| PQID | 1787258614 |
| PQPubID | 75722 |
| PageCount | 21 |
| ParticipantIDs | ieee_primary_7360912 crossref_primary_10_1109_JSTARS_2015_2502991 proquest_miscellaneous_1816025034 crossref_citationtrail_10_1109_JSTARS_2015_2502991 proquest_journals_1787258614 |
| PublicationCentury | 2000 |
| PublicationDate | 2016-March 2016-3-00 20160301 |
| PublicationDateYYYYMMDD | 2016-03-01 |
| PublicationDate_xml | – month: 03 year: 2016 text: 2016-March |
| PublicationDecade | 2010 |
| PublicationPlace | Piscataway |
| PublicationPlace_xml | – name: Piscataway |
| PublicationTitle | IEEE journal of selected topics in applied earth observations and remote sensing |
| PublicationTitleAbbrev | JSTARS |
| PublicationYear | 2016 |
| 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 | ref56 ref59 ref15 ref58 ref14 ref53 ref55 ref11 ref54 ref10 ref17 la (ref52) 2005; 5914 ref16 ref19 ref18 ref51 ref50 ref46 ref45 ref48 ref47 ref42 ref43 ref49 gou (ref13) 0 ref8 samant (ref12) 0 ref4 ref3 ref6 ref5 kim (ref41) 2002; 9 ref40 yu (ref9) 2008; 30 ref35 ref34 roweis (ref37) 2000; 290 ref36 ref31 ref30 aharon (ref22) 2006; 54 ref32 ref38 gao (ref44) 0 ng (ref33) 2002; 14 ref71 tang (ref57) 0 ref72 liu (ref39) 0 carrara (ref1) 1995 ref68 ref24 ref67 ref23 ref69 ref25 ref64 ref20 ref63 lin (ref61) 2010 ref65 ref21 xu (ref26) 0 ref28 ref27 bengio (ref70) 2006 ref29 zhu (ref66) 2006; 37 oliver (ref2) 2004 ref60 ref62 ma (ref7) 2011; 11 |
| References_xml | – volume: 5914 start-page: 273 year: 2005 ident: ref52 article-title: Signal reconstruction using sparse tree representations publication-title: Int Soc Opt Eng – ident: ref29 doi: 10.2528/PIER13041503 – ident: ref50 doi: 10.1109/CVPR.2005.38 – volume: 290 start-page: 2323 year: 2000 ident: ref37 article-title: Nonlinear dimensionality reduction by locally linear embedding publication-title: Science doi: 10.1126/science.290.5500.2323 – ident: ref62 doi: 10.1137/080716542 – ident: ref40 doi: 10.1109/ICIG.2011.86 – ident: ref16 doi: 10.1109/LGRS.2013.2292820 – ident: ref35 doi: 10.1162/089976603321780317 – ident: ref3 doi: 10.1109/36.35954 – start-page: 1 year: 0 ident: ref44 article-title: Kernel sparse representation for image classification and face recognition publication-title: Proc Eur Conf Comput Vision – volume: 14 start-page: 849 year: 2002 ident: ref33 article-title: On spectral clustering: Analysis and an algorithm publication-title: Adv Neural Inform Process Syst – start-page: 667 year: 0 ident: ref13 article-title: Image segmentation based on fusing multi-feature and spatial spectral clustering publication-title: Proc Congr Image Signal Process (CISP'08) – ident: ref46 doi: 10.1109/TGRS.2012.2201730 – start-page: 1 year: 0 ident: ref26 article-title: SAR image compression based on sparse representation publication-title: Proc Int Radar Symp (IRS'13) – ident: ref48 doi: 10.1109/TIP.2014.2322938 – ident: ref10 doi: 10.1109/IGARSS.2012.6351715 – ident: ref69 doi: 10.1016/j.neucom.2014.05.010 – ident: ref51 doi: 10.1080/2150704X.2012.723146 – ident: ref72 doi: 10.1109/TIP.2010.2040763 – ident: ref60 doi: 10.1016/j.neucom.2011.08.018 – ident: ref53 doi: 10.1109/83.951532 – volume: 37 start-page: 63 year: 2006 ident: ref66 article-title: Semi-supervised learning literature survey publication-title: Computer Sciences – start-page: 610 year: 0 ident: ref12 article-title: Segmentation technique of SAR imagery based on fuzzy c-means clustering publication-title: Proc Int Conf Adv Eng Sci Manage – start-page: 663 year: 0 ident: ref39 article-title: Robust subspace segmentation by low-rank representation publication-title: Proc Int Conf Mach Learn (ICML'10) – ident: ref45 doi: 10.1109/TSP.2011.2179539 – ident: ref42 doi: 10.1109/TCSVT.2003.818352 – ident: ref68 doi: 10.1109/IGARSS.2009.5417941 – ident: ref34 doi: 10.1109/ICCV.2011.6126528 – start-page: 193 year: 2006 ident: ref70 article-title: Label propagation and quadratic criterion publication-title: Semi-Supervised Learn doi: 10.7551/mitpress/6173.003.0016 – ident: ref36 doi: 10.1109/TPAMI.2008.216 – ident: ref25 doi: 10.1109/TIP.2010.2050625 – start-page: 377 year: 0 ident: ref57 article-title: A new local feature extraction in SAR image publication-title: Proc Int Asia-Pac Conf Synth Aperture Radar (APSAR) – ident: ref8 doi: 10.1109/TPAMI.2007.70844 – volume: 9 start-page: 40 year: 2002 ident: ref41 article-title: Face recognition using kernel principal component analysis publication-title: IEEE Signal Process Lett doi: 10.1109/97.991133 – ident: ref30 doi: 10.1007/s11045-013-0225-8 – ident: ref4 doi: 10.1109/TPAMI.2011.274 – ident: ref65 doi: 10.1109/34.868688 – ident: ref47 doi: 10.1007/978-3-642-33506-8_7 – volume: 30 start-page: 2126 year: 2008 ident: ref9 article-title: IRGS: Image segmentation using edge penalties and region growing publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2008.15 – ident: ref43 doi: 10.1109/TPAMI.2012.62 – year: 2004 ident: ref2 publication-title: Understanding Synthetic Aperture Radar Images – ident: ref32 doi: 10.1109/CVPR.2009.5206547 – ident: ref54 doi: 10.1016/j.ins.2011.02.025 – ident: ref14 doi: 10.1016/j.sigpro.2012.08.024 – ident: ref24 doi: 10.1109/TIT.2005.860430 – ident: ref5 doi: 10.1109/JSTARS.2012.2201249 – ident: ref38 doi: 10.1109/TIP.2009.2038764 – ident: ref64 doi: 10.1109/KAMW.2008.4810503 – ident: ref17 doi: 10.1109/36.673672 – ident: ref63 doi: 10.1016/j.patcog.2013.07.003 – ident: ref11 doi: 10.1109/ICNC.2009.447 – ident: ref20 doi: 10.1109/APSAR.2009.5374114 – ident: ref58 doi: 10.1109/APSAR.2009.5374117 – ident: ref31 doi: 10.1109/TPAMI.2008.79 – ident: ref56 doi: 10.1109/TPAMI.2012.120 – ident: ref49 doi: 10.1007/978-3-642-33506-8_55 – volume: 54 start-page: 4311 year: 2006 ident: ref22 article-title: K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation publication-title: IEEE Trans Signal Process doi: 10.1109/TSP.2006.881199 – ident: ref19 doi: 10.1109/JSTARS.2012.2230435 – ident: ref18 doi: 10.1109/TGRS.2012.2203604 – ident: ref55 doi: 10.1109/TPAMI.2009.96 – ident: ref28 doi: 10.1109/RADAR.2011.5960546 – ident: ref15 doi: 10.1109/TGRS.2013.2287273 – year: 2010 ident: ref61 article-title: The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices – ident: ref71 doi: 10.1109/TSMC.1973.4309314 – ident: ref59 doi: 10.1109/BIBE.2012.6399658 – ident: ref21 doi: 10.1109/TGRS.2008.918647 – ident: ref6 doi: 10.1109/JSTARS.2013.2255119 – volume: 11 start-page: 5205 year: 2011 ident: ref7 article-title: SAR image segmentation based on artificial bee colony algorithm publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2011.05.039 – ident: ref23 doi: 10.1109/JPROC.2010.2044470 – ident: ref27 doi: 10.1109/CVPR.2011.5995393 – ident: ref67 doi: 10.1109/TGRS.2007.895416 – year: 1995 ident: ref1 publication-title: Spotlight Synthetic Aperture Radar-Signal Processing Algorithms |
| SSID | ssj0062793 |
| Score | 2.2300653 |
| Snippet | Recently, more and more attention has been drawn on the study of sparse graph-based classification with respect to pattern recognition and computer vision.... |
| SourceID | proquest crossref ieee |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1265 |
| SubjectTerms | Algorithms Categories Clustering algorithms Feature extraction Graphs Image segmentation Local spatial relation multi-kernel Noise Pattern recognition Similarity sparse representation (SR) Speckle Synthetic aperture radar synthetic aperture radar (SAR) image segmentation |
| Title | A Multi-kernel Joint Sparse Graph for SAR Image Segmentation |
| URI | https://ieeexplore.ieee.org/document/7360912 https://www.proquest.com/docview/1787258614 https://www.proquest.com/docview/1816025034 |
| Volume | 9 |
| WOSCitedRecordID | wos000373054100025&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/eLvHCXMwlV1ZSwMxEB60KPjiLVarRPCxa_fKZgO-FNGqiEir4NuyTSZS1G3pIfjvnWS3BVEE3wKbvb4k33w5Zgbg1CSKI8lWz-Q89WKMOPGg1J4m2yhTE4u071r6Ttzfp8_P8mEJmgtfGER0h8_wzBbdXr4eqpldKmuJKCHzRoS7LIQofbXmrJuEwgXYJT0iPRsypoowFPiyRV283e3ZY1z8jCw-EXDwzQq5tCo_uNgZmKuN_33aJqxXQpK1y5bfgiUstmG14xL1fu7AeZs531rvFccFvrHb4aCYst6I5rHIOjZKNSO5ynrtLrt5J05hPXx5r_yQil14urp8vLj2qkwJnooif-ppQQM3IuUVKqOFMoFBpTFNuMljjLXRBHvgY07FELlUuYx1mPS5UX0_0okf7UGtGBa4D0zINCBVosIg92nqpvqx4sZmFiXDlSqDdQjnyGWqCiNus1m8ZW464cushDuzcGcV3HVoLm4alVE0_q6-YxFeVK3ArUNj3kRZNdImWUCME_KUVEYdThaXaYzYjY-8wOGM6tAvWa0XxQe_P_kQ1uj9SXm2rAG16XiGR7CiPqaDyfjYdbQvRtzN_A |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1ZS8QwEB7EA33xFtczgo9WeyRtA74s4r0u4ir4VrrJRBa1u-wh-O-dpN0FUQTfAk1L-iX55ssxMwCHJlYCSbZ6JhepxzESxINSe5pso0wNT9K26-lG0mymz8_yfgqOJr4wiOgun-GxLbqzfN1VI7tVdpJEMZk3ItwZwXkYlN5aY96Nw8SF2CVFIj0bNKaKMRT48oQGef2hZS9yiWOy-UTBwTc75BKr_GBjZ2Iulv7XuGVYrKQkq5d9vwJTWKzC3KVL1fu5Bqd15rxrvVfsF_jGbrqdYshaPVrJIru0caoZCVbWqj-w63diFdbCl_fKE6lYh6eL88ezK6_KleCpKPKHnk5o6kakvUJldKJMYFBpTGNhco5cG03ABz7mVAxRSJVLrsO4LYxq-5GO_WgDpotugZvAEpkGpEtUGOQ-Ld5UmythbG5RMl2pMliDcIxcpqpA4jafxVvmFhS-zEq4Mwt3VsFdg6PJS70yjsbf1dcswpOqFbg12Bl3UVbNtUEWEOeEIiWdUYODyWOaJfboIy-wO6I69EtW7UV86_cv78P81eNdI2tcN2-3YYHaEpc3zXZgetgf4S7Mqo9hZ9Dfc4PuCylG0UM |
| 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+Multi-kernel+Joint+Sparse+Graph+for+SAR+Image+Segmentation&rft.jtitle=IEEE+journal+of+selected+topics+in+applied+earth+observations+and+remote+sensing&rft.au=Gu%2C+Jing&rft.au=Jiao%2C+Licheng&rft.au=Yang%2C+Shuyuan&rft.au=Liu%2C+Fang&rft.date=2016-03-01&rft.issn=1939-1404&rft.eissn=2151-1535&rft.volume=9&rft.issue=3&rft.spage=1265&rft.epage=1285&rft_id=info:doi/10.1109%2FJSTARS.2015.2502991&rft.externalDBID=NO_FULL_TEXT |
| 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 |