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Nonlinear mixture analysis for hyperspectral imagery
Nonlinear mixture analysis for hyperspectral imagery is investigated in this paper. A simple but effective nonlinear mixture model is adopted, where the multiplication of each pair of endmembers results in another ¿endmember¿, representing nonlinear scattering effect during pixel construction proces...
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| container_end_page | III-832 |
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| container_start_page | III-829 |
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| container_volume | 3 |
| creator | Raksuntorn, N. Qian Du |
| description | Nonlinear mixture analysis for hyperspectral imagery is investigated in this paper. A simple but effective nonlinear mixture model is adopted, where the multiplication of each pair of endmembers results in another ¿endmember¿, representing nonlinear scattering effect during pixel construction process. The analysis is followed by original linear demixing process. Due to the larger number of nonlinear terms being added, the resulting abundance estimation may contain some error if most of endmembers do not really participate in the mixture of a pixel. We take advantage of the developed endmember variable linear mixture model (EVLMM) to search the actual endmember set for each pixel, which yields more accurate abundance estimation. |
| doi_str_mv | 10.1109/IGARSS.2009.5417895 |
| format | conference_proceeding |
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A simple but effective nonlinear mixture model is adopted, where the multiplication of each pair of endmembers results in another ¿endmember¿, representing nonlinear scattering effect during pixel construction process. The analysis is followed by original linear demixing process. Due to the larger number of nonlinear terms being added, the resulting abundance estimation may contain some error if most of endmembers do not really participate in the mixture of a pixel. We take advantage of the developed endmember variable linear mixture model (EVLMM) to search the actual endmember set for each pixel, which yields more accurate abundance estimation.</description><identifier>ISSN: 2153-6996</identifier><identifier>ISBN: 1424433940</identifier><identifier>ISBN: 9781424433940</identifier><identifier>EISSN: 2153-7003</identifier><identifier>EISBN: 1424433959</identifier><identifier>EISBN: 9781424433957</identifier><identifier>DOI: 10.1109/IGARSS.2009.5417895</identifier><identifier>LCCN: 2008910215</identifier><language>eng</language><publisher>IEEE</publisher><subject>hyperspectral imagery ; Hyperspectral imaging ; Image analysis ; Image reconstruction ; Image retrieval ; Information retrieval ; Minerals ; nonlinear spectral mixture analysis ; Scattering ; Soil ; Spectral analysis ; Yield estimation</subject><ispartof>2009 IEEE International Geoscience and Remote Sensing Symposium, 2009, Vol.3, p.III-829-III-832</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5417895$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54555,54920,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5417895$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Raksuntorn, N.</creatorcontrib><creatorcontrib>Qian Du</creatorcontrib><title>Nonlinear mixture analysis for hyperspectral imagery</title><title>2009 IEEE International Geoscience and Remote Sensing Symposium</title><addtitle>IGARSS</addtitle><description>Nonlinear mixture analysis for hyperspectral imagery is investigated in this paper. A simple but effective nonlinear mixture model is adopted, where the multiplication of each pair of endmembers results in another ¿endmember¿, representing nonlinear scattering effect during pixel construction process. The analysis is followed by original linear demixing process. Due to the larger number of nonlinear terms being added, the resulting abundance estimation may contain some error if most of endmembers do not really participate in the mixture of a pixel. We take advantage of the developed endmember variable linear mixture model (EVLMM) to search the actual endmember set for each pixel, which yields more accurate abundance estimation.</description><subject>hyperspectral imagery</subject><subject>Hyperspectral imaging</subject><subject>Image analysis</subject><subject>Image reconstruction</subject><subject>Image retrieval</subject><subject>Information retrieval</subject><subject>Minerals</subject><subject>nonlinear spectral mixture analysis</subject><subject>Scattering</subject><subject>Soil</subject><subject>Spectral analysis</subject><subject>Yield estimation</subject><issn>2153-6996</issn><issn>2153-7003</issn><isbn>1424433940</isbn><isbn>9781424433940</isbn><isbn>1424433959</isbn><isbn>9781424433957</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNpFkM1qwzAQhNWfQJM0T5CLX8DuSivJ3mMIbRoILTS5B9WVWhXHMZIL9dtXEEP3Mux8ywwsY0sOBedAD9vN6m2_LwQAFUrysiJ1xWZcCikRSdE1mwquMC8B8OYfSLgdgSbSEzZLARVxSNYdW8T4DWmkAi3LKZMv57bxrTUhO_nf_ifYzLSmGaKPmTuH7GvobIidrftgmsyfzKcNwz2bONNEuxh1zg5Pj4f1c7573WzXq13uCfpcOJTVuwJXo5JWp0JTk0HhVEXmg4MlVzlVI3IueNqJDBlpnU7HqDnhnC0vsd5ae-xCag_DcfwE_gFObEu2</recordid><startdate>200907</startdate><enddate>200907</enddate><creator>Raksuntorn, N.</creator><creator>Qian Du</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200907</creationdate><title>Nonlinear mixture analysis for hyperspectral imagery</title><author>Raksuntorn, N. ; Qian Du</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-2f348b50fc354e6506ac9a32f589ad10e9f8f5c331121d1099a9a4ef64e636193</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>hyperspectral imagery</topic><topic>Hyperspectral imaging</topic><topic>Image analysis</topic><topic>Image reconstruction</topic><topic>Image retrieval</topic><topic>Information retrieval</topic><topic>Minerals</topic><topic>nonlinear spectral mixture analysis</topic><topic>Scattering</topic><topic>Soil</topic><topic>Spectral analysis</topic><topic>Yield estimation</topic><toplevel>online_resources</toplevel><creatorcontrib>Raksuntorn, N.</creatorcontrib><creatorcontrib>Qian Du</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Raksuntorn, N.</au><au>Qian Du</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Nonlinear mixture analysis for hyperspectral imagery</atitle><btitle>2009 IEEE International Geoscience and Remote Sensing Symposium</btitle><stitle>IGARSS</stitle><date>2009-07</date><risdate>2009</risdate><volume>3</volume><spage>III-829</spage><epage>III-832</epage><pages>III-829-III-832</pages><issn>2153-6996</issn><eissn>2153-7003</eissn><isbn>1424433940</isbn><isbn>9781424433940</isbn><eisbn>1424433959</eisbn><eisbn>9781424433957</eisbn><abstract>Nonlinear mixture analysis for hyperspectral imagery is investigated in this paper. A simple but effective nonlinear mixture model is adopted, where the multiplication of each pair of endmembers results in another ¿endmember¿, representing nonlinear scattering effect during pixel construction process. The analysis is followed by original linear demixing process. Due to the larger number of nonlinear terms being added, the resulting abundance estimation may contain some error if most of endmembers do not really participate in the mixture of a pixel. We take advantage of the developed endmember variable linear mixture model (EVLMM) to search the actual endmember set for each pixel, which yields more accurate abundance estimation.</abstract><pub>IEEE</pub><doi>10.1109/IGARSS.2009.5417895</doi></addata></record> |
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| identifier | ISSN: 2153-6996 |
| ispartof | 2009 IEEE International Geoscience and Remote Sensing Symposium, 2009, Vol.3, p.III-829-III-832 |
| issn | 2153-6996 2153-7003 |
| language | eng |
| recordid | cdi_ieee_primary_5417895 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | hyperspectral imagery Hyperspectral imaging Image analysis Image reconstruction Image retrieval Information retrieval Minerals nonlinear spectral mixture analysis Scattering Soil Spectral analysis Yield estimation |
| title | Nonlinear mixture analysis for hyperspectral imagery |
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