Urban-Area Segmentation Using Visual Words
In this letter, we address the problem of urban-area extraction by using a feature-free image representation concept known as ldquoVisual Words.rdquo This method is based on building a ldquodictionaryrdquo of small patches, some of which appear mainly in urban areas. The proposed algorithm is based...
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| Veröffentlicht in: | IEEE geoscience and remote sensing letters 2009-07, Vol.6 (3), p.388-392 |
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| container_title | IEEE geoscience and remote sensing letters |
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| creator | Weizman, L. Goldberger, J. |
| description | In this letter, we address the problem of urban-area extraction by using a feature-free image representation concept known as ldquoVisual Words.rdquo This method is based on building a ldquodictionaryrdquo of small patches, some of which appear mainly in urban areas. The proposed algorithm is based on a new pixel-level variant of visual words and is based on three parts: building a visual dictionary, learning urban words from labeled images, and detecting urban regions in a new image. Using normalized patches makes the method more robust to changes in illumination during acquisition time. The improved performance of the method is demonstrated on real satellite images from three different sensors: LANDSAT, SPOT, and IKONOS. To assess the robustness of our method, the learning and testing procedures were carried out on different and independent images. |
| doi_str_mv | 10.1109/LGRS.2009.2014400 |
| format | Article |
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The proposed algorithm is based on a new pixel-level variant of visual words and is based on three parts: building a visual dictionary, learning urban words from labeled images, and detecting urban regions in a new image. Using normalized patches makes the method more robust to changes in illumination during acquisition time. The improved performance of the method is demonstrated on real satellite images from three different sensors: LANDSAT, SPOT, and IKONOS. To assess the robustness of our method, the learning and testing procedures were carried out on different and independent images.</description><identifier>ISSN: 1545-598X</identifier><identifier>EISSN: 1558-0571</identifier><identifier>DOI: 10.1109/LGRS.2009.2014400</identifier><identifier>CODEN: IGRSBY</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Dictionaries ; Illumination ; Image representation ; Image segmentation ; Image sensors ; Learning ; Lighting ; Map updating ; object detection ; Pixel ; Remote sensing ; Representations ; Robustness ; Satellites ; Segmentation ; Urban areas ; Visual ; visual words</subject><ispartof>IEEE geoscience and remote sensing letters, 2009-07, Vol.6 (3), p.388-392</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2009</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c398t-fc75e89cd425b36ec643d6c07f6b3f6920940aac0ba56fabc124f0fd726dddd03</citedby><cites>FETCH-LOGICAL-c398t-fc75e89cd425b36ec643d6c07f6b3f6920940aac0ba56fabc124f0fd726dddd03</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4787085$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,781,785,797,27929,27930,54763</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4787085$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Weizman, L.</creatorcontrib><creatorcontrib>Goldberger, J.</creatorcontrib><title>Urban-Area Segmentation Using Visual Words</title><title>IEEE geoscience and remote sensing letters</title><addtitle>LGRS</addtitle><description>In this letter, we address the problem of urban-area extraction by using a feature-free image representation concept known as ldquoVisual Words.rdquo This method is based on building a ldquodictionaryrdquo of small patches, some of which appear mainly in urban areas. The proposed algorithm is based on a new pixel-level variant of visual words and is based on three parts: building a visual dictionary, learning urban words from labeled images, and detecting urban regions in a new image. Using normalized patches makes the method more robust to changes in illumination during acquisition time. The improved performance of the method is demonstrated on real satellite images from three different sensors: LANDSAT, SPOT, and IKONOS. To assess the robustness of our method, the learning and testing procedures were carried out on different and independent images.</description><subject>Dictionaries</subject><subject>Illumination</subject><subject>Image representation</subject><subject>Image segmentation</subject><subject>Image sensors</subject><subject>Learning</subject><subject>Lighting</subject><subject>Map updating</subject><subject>object detection</subject><subject>Pixel</subject><subject>Remote sensing</subject><subject>Representations</subject><subject>Robustness</subject><subject>Satellites</subject><subject>Segmentation</subject><subject>Urban areas</subject><subject>Visual</subject><subject>visual words</subject><issn>1545-598X</issn><issn>1558-0571</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9kE1Lw0AQhhdRsFZ_gHgJHhSE1Nmv7O6xFK1CQbBWvS2bzW5JSZO6mxz89ya0ePDgHGbm8LwD8yB0iWGCMaj7xfx1OSEAqm-YMYAjNMKcyxS4wMfDznjKlfw8RWcxbgAIk1KM0N0q5KZOp8GZZOnWW1e3pi2bOlnFsl4n72XsTJV8NKGI5-jEmyq6i8Mco9Xjw9vsKV28zJ9n00VqqZJt6q3gTipbMMJzmjmbMVpkFoTPcuozRUAxMMZCbnjmTW4xYR58IUhW9AV0jG73d3eh-epcbPW2jNZVlald00UtBQdKCMM9efMvSTkQwYjswes_4KbpQt1_oWWGBcVEsR7Ce8iGJsbgvN6FcmvCt8agB8l6kKwHyfoguc9c7TOlc-6XZ0IKkJz-AMxQdrY</recordid><startdate>20090701</startdate><enddate>20090701</enddate><creator>Weizman, L.</creator><creator>Goldberger, J.</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>20090701</creationdate><title>Urban-Area Segmentation Using Visual Words</title><author>Weizman, L. ; Goldberger, J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c398t-fc75e89cd425b36ec643d6c07f6b3f6920940aac0ba56fabc124f0fd726dddd03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Dictionaries</topic><topic>Illumination</topic><topic>Image representation</topic><topic>Image segmentation</topic><topic>Image sensors</topic><topic>Learning</topic><topic>Lighting</topic><topic>Map updating</topic><topic>object detection</topic><topic>Pixel</topic><topic>Remote sensing</topic><topic>Representations</topic><topic>Robustness</topic><topic>Satellites</topic><topic>Segmentation</topic><topic>Urban areas</topic><topic>Visual</topic><topic>visual words</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Weizman, L.</creatorcontrib><creatorcontrib>Goldberger, J.</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>Weizman, L.</au><au>Goldberger, J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Urban-Area Segmentation Using Visual Words</atitle><jtitle>IEEE geoscience and remote sensing letters</jtitle><stitle>LGRS</stitle><date>2009-07-01</date><risdate>2009</risdate><volume>6</volume><issue>3</issue><spage>388</spage><epage>392</epage><pages>388-392</pages><issn>1545-598X</issn><eissn>1558-0571</eissn><coden>IGRSBY</coden><abstract>In this letter, we address the problem of urban-area extraction by using a feature-free image representation concept known as ldquoVisual Words.rdquo This method is based on building a ldquodictionaryrdquo of small patches, some of which appear mainly in urban areas. The proposed algorithm is based on a new pixel-level variant of visual words and is based on three parts: building a visual dictionary, learning urban words from labeled images, and detecting urban regions in a new image. Using normalized patches makes the method more robust to changes in illumination during acquisition time. The improved performance of the method is demonstrated on real satellite images from three different sensors: LANDSAT, SPOT, and IKONOS. To assess the robustness of our method, the learning and testing procedures were carried out on different and independent images.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/LGRS.2009.2014400</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, 2009-07, Vol.6 (3), p.388-392 |
| issn | 1545-598X 1558-0571 |
| language | eng |
| recordid | cdi_crossref_primary_10_1109_LGRS_2009_2014400 |
| source | IEEE Electronic Library (IEL) |
| subjects | Dictionaries Illumination Image representation Image segmentation Image sensors Learning Lighting Map updating object detection Pixel Remote sensing Representations Robustness Satellites Segmentation Urban areas Visual visual words |
| title | Urban-Area Segmentation Using Visual Words |
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