Classification and Extraction of Spatial Features in Urban Areas Using High-Resolution Multispectral Imagery
Classification and extraction of spatial features are investigated in urban areas from high spatial resolution multispectral imagery. The proposed approach consists of three steps. First, as an extension of our previous work [pixel shape index (PSI)], a structural feature set (SFS) is proposed to ex...
Gespeichert in:
| Veröffentlicht in: | IEEE geoscience and remote sensing letters 2007-04, Vol.4 (2), p.260-264 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 264 |
|---|---|
| container_issue | 2 |
| container_start_page | 260 |
| container_title | IEEE geoscience and remote sensing letters |
| container_volume | 4 |
| creator | Huang, Xin Zhang, Liangpei Li, Pingxiang |
| description | Classification and extraction of spatial features are investigated in urban areas from high spatial resolution multispectral imagery. The proposed approach consists of three steps. First, as an extension of our previous work [pixel shape index (PSI)], a structural feature set (SFS) is proposed to extract the statistical features of the direction-lines histogram. Second, some methods of dimension reduction, including independent component analysis, decision boundary feature extraction, and the similarity-index feature selection, are implemented for the proposed SFS to reduce information redundancy. Third, four classifiers, the maximum-likelihood classifier, backpropagation neural network, probability neural network based on expectation-maximization training, and support vector machine, are compared to assess SFS and other spatial feature sets. We evaluate the proposed approach on two QuickBird datasets, and the results show that the new set of reduced spatial features has better performance than the existing length-width extraction algorithm and PSI |
| doi_str_mv | 10.1109/LGRS.2006.890540 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_crossref_primary_10_1109_LGRS_2006_890540</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>4156157</ieee_id><sourcerecordid>903614531</sourcerecordid><originalsourceid>FETCH-LOGICAL-c526t-8b72938777e79c22394e9dc5afd39de4716981248eafb4e6e2b0387ed22bcc913</originalsourceid><addsrcrecordid>eNqF0cuLFDEQB-AgCq6jd8FL8KBeesz7cVyGfcGIsOuAt5BOV49ZMt1j0g3uf296Rzx4WE9JyFdFJT-E3lKyppTYz9ur27s1I0StjSVSkGfojEppGiI1fb7shWykNd9folel3BPChDH6DKVN8qXEPgY_xXHAfujwxa8p-_B4HHt8d6w3PuFL8NOcoeA44F1u_YDPM_iCdyUOe3wd9z-aWyhjmh8Lv8xpiuUIobZK-Obg95AfXqMXvU8F3vxZV2h3efFtc91sv17dbM63TZBMTY1pNbPcaK1B28AYtwJsF6TvO247EJoqa2h9APi-FaCAtaRy6BhrQ7CUr9DHU99jHn_OUCZ3iCVASn6AcS7OEq6okPz_0hiiLNOGV_nhSckFt0bVuVfo05OQasUok1Iv9P0_9H6c81C_xhklbBVMV0ROKOSxlAy9O-Z48PnBUeKW6N0SvVuid6foa8m7U0kEgL9cUKmo1Pw3JYGpwQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>864979327</pqid></control><display><type>article</type><title>Classification and Extraction of Spatial Features in Urban Areas Using High-Resolution Multispectral Imagery</title><source>IEEE Electronic Library (IEL)</source><creator>Huang, Xin ; Zhang, Liangpei ; Li, Pingxiang</creator><creatorcontrib>Huang, Xin ; Zhang, Liangpei ; Li, Pingxiang</creatorcontrib><description>Classification and extraction of spatial features are investigated in urban areas from high spatial resolution multispectral imagery. The proposed approach consists of three steps. First, as an extension of our previous work [pixel shape index (PSI)], a structural feature set (SFS) is proposed to extract the statistical features of the direction-lines histogram. Second, some methods of dimension reduction, including independent component analysis, decision boundary feature extraction, and the similarity-index feature selection, are implemented for the proposed SFS to reduce information redundancy. Third, four classifiers, the maximum-likelihood classifier, backpropagation neural network, probability neural network based on expectation-maximization training, and support vector machine, are compared to assess SFS and other spatial feature sets. We evaluate the proposed approach on two QuickBird datasets, and the results show that the new set of reduced spatial features has better performance than the existing length-width extraction algorithm and PSI</description><identifier>ISSN: 1545-598X</identifier><identifier>EISSN: 1558-0571</identifier><identifier>DOI: 10.1109/LGRS.2006.890540</identifier><identifier>CODEN: IGRSBY</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Algorithms ; Backpropagation ; Classification ; Classifiers ; Data mining ; Extraction ; Feature extraction ; feature selection ; highspatial resolution multispectral (HSRM) imagery ; Histograms ; Imagery ; Independent component analysis ; Multispectral imaging ; Neural networks ; Redundancy ; Shape ; spatial feature set ; Spatial resolution ; Urban areas</subject><ispartof>IEEE geoscience and remote sensing letters, 2007-04, Vol.4 (2), p.260-264</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2007</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c526t-8b72938777e79c22394e9dc5afd39de4716981248eafb4e6e2b0387ed22bcc913</citedby><cites>FETCH-LOGICAL-c526t-8b72938777e79c22394e9dc5afd39de4716981248eafb4e6e2b0387ed22bcc913</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4156157$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27923,27924,54757</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4156157$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Huang, Xin</creatorcontrib><creatorcontrib>Zhang, Liangpei</creatorcontrib><creatorcontrib>Li, Pingxiang</creatorcontrib><title>Classification and Extraction of Spatial Features in Urban Areas Using High-Resolution Multispectral Imagery</title><title>IEEE geoscience and remote sensing letters</title><addtitle>LGRS</addtitle><description>Classification and extraction of spatial features are investigated in urban areas from high spatial resolution multispectral imagery. The proposed approach consists of three steps. First, as an extension of our previous work [pixel shape index (PSI)], a structural feature set (SFS) is proposed to extract the statistical features of the direction-lines histogram. Second, some methods of dimension reduction, including independent component analysis, decision boundary feature extraction, and the similarity-index feature selection, are implemented for the proposed SFS to reduce information redundancy. Third, four classifiers, the maximum-likelihood classifier, backpropagation neural network, probability neural network based on expectation-maximization training, and support vector machine, are compared to assess SFS and other spatial feature sets. We evaluate the proposed approach on two QuickBird datasets, and the results show that the new set of reduced spatial features has better performance than the existing length-width extraction algorithm and PSI</description><subject>Algorithms</subject><subject>Backpropagation</subject><subject>Classification</subject><subject>Classifiers</subject><subject>Data mining</subject><subject>Extraction</subject><subject>Feature extraction</subject><subject>feature selection</subject><subject>highspatial resolution multispectral (HSRM) imagery</subject><subject>Histograms</subject><subject>Imagery</subject><subject>Independent component analysis</subject><subject>Multispectral imaging</subject><subject>Neural networks</subject><subject>Redundancy</subject><subject>Shape</subject><subject>spatial feature set</subject><subject>Spatial resolution</subject><subject>Urban areas</subject><issn>1545-598X</issn><issn>1558-0571</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqF0cuLFDEQB-AgCq6jd8FL8KBeesz7cVyGfcGIsOuAt5BOV49ZMt1j0g3uf296Rzx4WE9JyFdFJT-E3lKyppTYz9ur27s1I0StjSVSkGfojEppGiI1fb7shWykNd9folel3BPChDH6DKVN8qXEPgY_xXHAfujwxa8p-_B4HHt8d6w3PuFL8NOcoeA44F1u_YDPM_iCdyUOe3wd9z-aWyhjmh8Lv8xpiuUIobZK-Obg95AfXqMXvU8F3vxZV2h3efFtc91sv17dbM63TZBMTY1pNbPcaK1B28AYtwJsF6TvO247EJoqa2h9APi-FaCAtaRy6BhrQ7CUr9DHU99jHn_OUCZ3iCVASn6AcS7OEq6okPz_0hiiLNOGV_nhSckFt0bVuVfo05OQasUok1Iv9P0_9H6c81C_xhklbBVMV0ROKOSxlAy9O-Z48PnBUeKW6N0SvVuid6foa8m7U0kEgL9cUKmo1Pw3JYGpwQ</recordid><startdate>20070401</startdate><enddate>20070401</enddate><creator>Huang, Xin</creator><creator>Zhang, Liangpei</creator><creator>Li, Pingxiang</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>7SC</scope><scope>7SP</scope><scope>7TG</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>JQ2</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope></search><sort><creationdate>20070401</creationdate><title>Classification and Extraction of Spatial Features in Urban Areas Using High-Resolution Multispectral Imagery</title><author>Huang, Xin ; Zhang, Liangpei ; Li, Pingxiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c526t-8b72938777e79c22394e9dc5afd39de4716981248eafb4e6e2b0387ed22bcc913</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Algorithms</topic><topic>Backpropagation</topic><topic>Classification</topic><topic>Classifiers</topic><topic>Data mining</topic><topic>Extraction</topic><topic>Feature extraction</topic><topic>feature selection</topic><topic>highspatial resolution multispectral (HSRM) imagery</topic><topic>Histograms</topic><topic>Imagery</topic><topic>Independent component analysis</topic><topic>Multispectral imaging</topic><topic>Neural networks</topic><topic>Redundancy</topic><topic>Shape</topic><topic>spatial feature set</topic><topic>Spatial resolution</topic><topic>Urban areas</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Huang, Xin</creatorcontrib><creatorcontrib>Zhang, Liangpei</creatorcontrib><creatorcontrib>Li, Pingxiang</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>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</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>ProQuest Computer Science Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>IEEE geoscience and remote sensing letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Huang, Xin</au><au>Zhang, Liangpei</au><au>Li, Pingxiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Classification and Extraction of Spatial Features in Urban Areas Using High-Resolution Multispectral Imagery</atitle><jtitle>IEEE geoscience and remote sensing letters</jtitle><stitle>LGRS</stitle><date>2007-04-01</date><risdate>2007</risdate><volume>4</volume><issue>2</issue><spage>260</spage><epage>264</epage><pages>260-264</pages><issn>1545-598X</issn><eissn>1558-0571</eissn><coden>IGRSBY</coden><abstract>Classification and extraction of spatial features are investigated in urban areas from high spatial resolution multispectral imagery. The proposed approach consists of three steps. First, as an extension of our previous work [pixel shape index (PSI)], a structural feature set (SFS) is proposed to extract the statistical features of the direction-lines histogram. Second, some methods of dimension reduction, including independent component analysis, decision boundary feature extraction, and the similarity-index feature selection, are implemented for the proposed SFS to reduce information redundancy. Third, four classifiers, the maximum-likelihood classifier, backpropagation neural network, probability neural network based on expectation-maximization training, and support vector machine, are compared to assess SFS and other spatial feature sets. We evaluate the proposed approach on two QuickBird datasets, and the results show that the new set of reduced spatial features has better performance than the existing length-width extraction algorithm and PSI</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/LGRS.2006.890540</doi><tpages>5</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1545-598X |
| ispartof | IEEE geoscience and remote sensing letters, 2007-04, Vol.4 (2), p.260-264 |
| issn | 1545-598X 1558-0571 |
| language | eng |
| recordid | cdi_crossref_primary_10_1109_LGRS_2006_890540 |
| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Backpropagation Classification Classifiers Data mining Extraction Feature extraction feature selection highspatial resolution multispectral (HSRM) imagery Histograms Imagery Independent component analysis Multispectral imaging Neural networks Redundancy Shape spatial feature set Spatial resolution Urban areas |
| title | Classification and Extraction of Spatial Features in Urban Areas Using High-Resolution Multispectral Imagery |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-11T11%3A47%3A01IST&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=Classification%20and%20Extraction%20of%20Spatial%20Features%20in%20Urban%20Areas%20Using%20High-Resolution%20Multispectral%20Imagery&rft.jtitle=IEEE%20geoscience%20and%20remote%20sensing%20letters&rft.au=Huang,%20Xin&rft.date=2007-04-01&rft.volume=4&rft.issue=2&rft.spage=260&rft.epage=264&rft.pages=260-264&rft.issn=1545-598X&rft.eissn=1558-0571&rft.coden=IGRSBY&rft_id=info:doi/10.1109/LGRS.2006.890540&rft_dat=%3Cproquest_RIE%3E903614531%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=864979327&rft_id=info:pmid/&rft_ieee_id=4156157&rfr_iscdi=true |