Unsupervised Hyperspectral Band Selection by Dominant Set Extraction
Unsupervised hyperspectral band selection has been an important topic in hyperspectral imagery. This technique aims at selecting some critical and decisive spectral bands from an original image for compact representation without compromising and distorting the raw information in the relevant spectra...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on geoscience and remote sensing 2016-01, Vol.54 (1), p.227-239 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 239 |
|---|---|
| container_issue | 1 |
| container_start_page | 227 |
| container_title | IEEE transactions on geoscience and remote sensing |
| container_volume | 54 |
| creator | Zhu, Guokang Huang, Yuancheng Lei, Jingsheng Bi, Zhongqin Xu, Feifei |
| description | Unsupervised hyperspectral band selection has been an important topic in hyperspectral imagery. This technique aims at selecting some critical and decisive spectral bands from an original image for compact representation without compromising and distorting the raw information in the relevant spectral bands. Although many efforts have been made to this topic, the structural information has not yet been well exploited during band selection, and there are still several deficiencies in search strategies, leaving room for further improvement. This paper tackles the unsupervised hyperspectral band selection problem from a global perspective and proposes a novel method claiming the following main contributions: structure-aware measures for band informativeness and independence; and a graph formulation of band selection allowing for an efficient integrated search by means of dominant set extraction. Experiments on three real hyperspectral images demonstrate the superiority of the proposed band selector in comparison with benchmark methods. |
| doi_str_mv | 10.1109/TGRS.2015.2453362 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_journals_1733173908</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>7166333</ieee_id><sourcerecordid>1778028478</sourcerecordid><originalsourceid>FETCH-LOGICAL-c396t-a4d7ba8e5d5b89eaae193b6c73d4f4123bfcce57deca54d0575533c02a294c313</originalsourceid><addsrcrecordid>eNpdkM9LwzAUgIMoOKd_gHgpePHSmZ9Nc9RtbsJAcNs5pOkrdHRtTVrZ_ntTNzwYeCQv-V5470PonuAJIVg9bxaf6wnFREwoF4wl9AKNiBBpjBPOL9EIE5XENFX0Gt14v8OYcEHkCM22te9bcN-lhzxaHsPRt2A7Z6ro1dR5tIYqpGVTR9kxmjX7sjZ1F267aH4I1O_TLboqTOXh7ryP0fZtvpku49XH4n36sootU0kXG57LzKQgcpGlCowBoliWWMlyXnBCWVZYC0LmYI3gORZShFEspoYqbhlhY_R0-rd1zVcPvtP70luoKlND03tNpEwxTblMA_r4D901vatDd4FiLITCA0VOlHWN9w4K3bpyb9xRE6wHr3rwqgev-uw11DycakoA-OMlSRIW1g80XHPo</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1733173908</pqid></control><display><type>article</type><title>Unsupervised Hyperspectral Band Selection by Dominant Set Extraction</title><source>IEEE Electronic Library (IEL)</source><creator>Zhu, Guokang ; Huang, Yuancheng ; Lei, Jingsheng ; Bi, Zhongqin ; Xu, Feifei</creator><creatorcontrib>Zhu, Guokang ; Huang, Yuancheng ; Lei, Jingsheng ; Bi, Zhongqin ; Xu, Feifei</creatorcontrib><description>Unsupervised hyperspectral band selection has been an important topic in hyperspectral imagery. This technique aims at selecting some critical and decisive spectral bands from an original image for compact representation without compromising and distorting the raw information in the relevant spectral bands. Although many efforts have been made to this topic, the structural information has not yet been well exploited during band selection, and there are still several deficiencies in search strategies, leaving room for further improvement. This paper tackles the unsupervised hyperspectral band selection problem from a global perspective and proposes a novel method claiming the following main contributions: structure-aware measures for band informativeness and independence; and a graph formulation of band selection allowing for an efficient integrated search by means of dominant set extraction. Experiments on three real hyperspectral images demonstrate the superiority of the proposed band selector in comparison with benchmark methods.</description><identifier>ISSN: 0196-2892</identifier><identifier>EISSN: 1558-0644</identifier><identifier>DOI: 10.1109/TGRS.2015.2453362</identifier><identifier>CODEN: IGRSD2</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Band selection ; Banded structure ; Correlation ; Distortion ; dominant set ; Extraction ; Feature extraction ; hyperspectral imagery ; Hyperspectral imaging ; image representation ; Indexes ; Redundancy ; remote sensing ; Satellites ; Search problems ; Searching ; Selectors ; Spectral bands ; Strategy</subject><ispartof>IEEE transactions on geoscience and remote sensing, 2016-01, Vol.54 (1), p.227-239</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Jan 2016</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c396t-a4d7ba8e5d5b89eaae193b6c73d4f4123bfcce57deca54d0575533c02a294c313</citedby><cites>FETCH-LOGICAL-c396t-a4d7ba8e5d5b89eaae193b6c73d4f4123bfcce57deca54d0575533c02a294c313</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/7166333$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7166333$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zhu, Guokang</creatorcontrib><creatorcontrib>Huang, Yuancheng</creatorcontrib><creatorcontrib>Lei, Jingsheng</creatorcontrib><creatorcontrib>Bi, Zhongqin</creatorcontrib><creatorcontrib>Xu, Feifei</creatorcontrib><title>Unsupervised Hyperspectral Band Selection by Dominant Set Extraction</title><title>IEEE transactions on geoscience and remote sensing</title><addtitle>TGRS</addtitle><description>Unsupervised hyperspectral band selection has been an important topic in hyperspectral imagery. This technique aims at selecting some critical and decisive spectral bands from an original image for compact representation without compromising and distorting the raw information in the relevant spectral bands. Although many efforts have been made to this topic, the structural information has not yet been well exploited during band selection, and there are still several deficiencies in search strategies, leaving room for further improvement. This paper tackles the unsupervised hyperspectral band selection problem from a global perspective and proposes a novel method claiming the following main contributions: structure-aware measures for band informativeness and independence; and a graph formulation of band selection allowing for an efficient integrated search by means of dominant set extraction. Experiments on three real hyperspectral images demonstrate the superiority of the proposed band selector in comparison with benchmark methods.</description><subject>Band selection</subject><subject>Banded structure</subject><subject>Correlation</subject><subject>Distortion</subject><subject>dominant set</subject><subject>Extraction</subject><subject>Feature extraction</subject><subject>hyperspectral imagery</subject><subject>Hyperspectral imaging</subject><subject>image representation</subject><subject>Indexes</subject><subject>Redundancy</subject><subject>remote sensing</subject><subject>Satellites</subject><subject>Search problems</subject><subject>Searching</subject><subject>Selectors</subject><subject>Spectral bands</subject><subject>Strategy</subject><issn>0196-2892</issn><issn>1558-0644</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkM9LwzAUgIMoOKd_gHgpePHSmZ9Nc9RtbsJAcNs5pOkrdHRtTVrZ_ntTNzwYeCQv-V5470PonuAJIVg9bxaf6wnFREwoF4wl9AKNiBBpjBPOL9EIE5XENFX0Gt14v8OYcEHkCM22te9bcN-lhzxaHsPRt2A7Z6ro1dR5tIYqpGVTR9kxmjX7sjZ1F267aH4I1O_TLboqTOXh7ryP0fZtvpku49XH4n36sootU0kXG57LzKQgcpGlCowBoliWWMlyXnBCWVZYC0LmYI3gORZShFEspoYqbhlhY_R0-rd1zVcPvtP70luoKlND03tNpEwxTblMA_r4D901vatDd4FiLITCA0VOlHWN9w4K3bpyb9xRE6wHr3rwqgev-uw11DycakoA-OMlSRIW1g80XHPo</recordid><startdate>20160101</startdate><enddate>20160101</enddate><creator>Zhu, Guokang</creator><creator>Huang, Yuancheng</creator><creator>Lei, Jingsheng</creator><creator>Bi, Zhongqin</creator><creator>Xu, Feifei</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><scope>7SP</scope><scope>F28</scope></search><sort><creationdate>20160101</creationdate><title>Unsupervised Hyperspectral Band Selection by Dominant Set Extraction</title><author>Zhu, Guokang ; Huang, Yuancheng ; Lei, Jingsheng ; Bi, Zhongqin ; Xu, Feifei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c396t-a4d7ba8e5d5b89eaae193b6c73d4f4123bfcce57deca54d0575533c02a294c313</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Band selection</topic><topic>Banded structure</topic><topic>Correlation</topic><topic>Distortion</topic><topic>dominant set</topic><topic>Extraction</topic><topic>Feature extraction</topic><topic>hyperspectral imagery</topic><topic>Hyperspectral imaging</topic><topic>image representation</topic><topic>Indexes</topic><topic>Redundancy</topic><topic>remote sensing</topic><topic>Satellites</topic><topic>Search problems</topic><topic>Searching</topic><topic>Selectors</topic><topic>Spectral bands</topic><topic>Strategy</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhu, Guokang</creatorcontrib><creatorcontrib>Huang, Yuancheng</creatorcontrib><creatorcontrib>Lei, Jingsheng</creatorcontrib><creatorcontrib>Bi, Zhongqin</creatorcontrib><creatorcontrib>Xu, Feifei</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Electronics & Communications Abstracts</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>IEEE transactions on geoscience and remote sensing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zhu, Guokang</au><au>Huang, Yuancheng</au><au>Lei, Jingsheng</au><au>Bi, Zhongqin</au><au>Xu, Feifei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Unsupervised Hyperspectral Band Selection by Dominant Set Extraction</atitle><jtitle>IEEE transactions on geoscience and remote sensing</jtitle><stitle>TGRS</stitle><date>2016-01-01</date><risdate>2016</risdate><volume>54</volume><issue>1</issue><spage>227</spage><epage>239</epage><pages>227-239</pages><issn>0196-2892</issn><eissn>1558-0644</eissn><coden>IGRSD2</coden><abstract>Unsupervised hyperspectral band selection has been an important topic in hyperspectral imagery. This technique aims at selecting some critical and decisive spectral bands from an original image for compact representation without compromising and distorting the raw information in the relevant spectral bands. Although many efforts have been made to this topic, the structural information has not yet been well exploited during band selection, and there are still several deficiencies in search strategies, leaving room for further improvement. This paper tackles the unsupervised hyperspectral band selection problem from a global perspective and proposes a novel method claiming the following main contributions: structure-aware measures for band informativeness and independence; and a graph formulation of band selection allowing for an efficient integrated search by means of dominant set extraction. Experiments on three real hyperspectral images demonstrate the superiority of the proposed band selector in comparison with benchmark methods.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TGRS.2015.2453362</doi><tpages>13</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 0196-2892 |
| ispartof | IEEE transactions on geoscience and remote sensing, 2016-01, Vol.54 (1), p.227-239 |
| issn | 0196-2892 1558-0644 |
| language | eng |
| recordid | cdi_proquest_journals_1733173908 |
| source | IEEE Electronic Library (IEL) |
| subjects | Band selection Banded structure Correlation Distortion dominant set Extraction Feature extraction hyperspectral imagery Hyperspectral imaging image representation Indexes Redundancy remote sensing Satellites Search problems Searching Selectors Spectral bands Strategy |
| title | Unsupervised Hyperspectral Band Selection by Dominant Set Extraction |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-08T08%3A23%3A19IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Unsupervised%20Hyperspectral%20Band%20Selection%20by%20Dominant%20Set%20Extraction&rft.jtitle=IEEE%20transactions%20on%20geoscience%20and%20remote%20sensing&rft.au=Zhu,%20Guokang&rft.date=2016-01-01&rft.volume=54&rft.issue=1&rft.spage=227&rft.epage=239&rft.pages=227-239&rft.issn=0196-2892&rft.eissn=1558-0644&rft.coden=IGRSD2&rft_id=info:doi/10.1109/TGRS.2015.2453362&rft_dat=%3Cproquest_RIE%3E1778028478%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1733173908&rft_id=info:pmid/&rft_ieee_id=7166333&rfr_iscdi=true |