A classification enhancement in hyperspectral imagery using superresolution technique
In this paper an improved supervised classification technique through applying a superresolution method on remotely sensed hyperspectral images is introduced. Superresolution methods provide high-resolution images from a sequence of low-resolution frames. In the proposed technique, classification of...
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| creator | Mianji, F.A. Ye Zhang Hosseinipanah, M. |
| description | In this paper an improved supervised classification technique through applying a superresolution method on remotely sensed hyperspectral images is introduced. Superresolution methods provide high-resolution images from a sequence of low-resolution frames. In the proposed technique, classification of the hyperspectral image is carried out using spectrally homogenous training classes of pixels. Low spatial resolution frames of different wavelengths of the hyperspectral image are fed to a quadratic programming based classification algorithm to enhance the spatial resolution of the classification process. The results show a better classification and an edge improvement. Target recognition is the main field which can benefit from this technique. |
| doi_str_mv | 10.1109/ICOSP.2008.4697296 |
| format | Conference Proceeding |
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Superresolution methods provide high-resolution images from a sequence of low-resolution frames. In the proposed technique, classification of the hyperspectral image is carried out using spectrally homogenous training classes of pixels. Low spatial resolution frames of different wavelengths of the hyperspectral image are fed to a quadratic programming based classification algorithm to enhance the spatial resolution of the classification process. The results show a better classification and an edge improvement. 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Target recognition is the main field which can benefit from this technique.</description><subject>Hyperspectral imagery</subject><subject>Hyperspectral imaging</subject><subject>Hyperspectral sensors</subject><subject>Image resolution</subject><subject>Layout</subject><subject>Pixel</subject><subject>Quadratic programming</subject><subject>Reflectivity</subject><subject>Remote sensing</subject><subject>Spatial resolution</subject><subject>Superresolution</subject><subject>Supervised classification</subject><subject>Target recognition</subject><issn>2164-5221</issn><isbn>1424421780</isbn><isbn>9781424421787</isbn><isbn>1424421799</isbn><isbn>9781424421794</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFkM9KAzEYxCNasK19Ab3kBbbmy2azybEU_xQKFbTnkk2-dCPbtG52D_v2Vi14GobhNwxDyD2wOQDTj6vl5v1tzhlTcyF1ybW8IhMQXAgOpdbX_0axGzLmIEVWcA4jMvmBNAOQ5S2ZpfTJGMtBKZnLMdkuqG1MSsEHa7pwjBRjbaLFA8aOhkjr4YRtOqHtWtPQcDB7bAfapxD3NPXnrMV0bPpftENbx_DV4x0ZedMknF10SrbPTx_L12y9eVktF-ssQFl0GVaCGWe1R8YL7aw3QiiHyoIqrXfAC-8KwyuXC1cJrJRVRmPlrAMtwZt8Sh7-egMi7k7teV477C7_5N80n1lo</recordid><startdate>200810</startdate><enddate>200810</enddate><creator>Mianji, F.A.</creator><creator>Ye Zhang</creator><creator>Hosseinipanah, M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200810</creationdate><title>A classification enhancement in hyperspectral imagery using superresolution technique</title><author>Mianji, F.A. ; Ye Zhang ; Hosseinipanah, M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-eb40adc9fe0259dcfa448de8c187cfd125fd5a2bd34db4eb8c8a9ebdcd1961fa3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Hyperspectral imagery</topic><topic>Hyperspectral imaging</topic><topic>Hyperspectral sensors</topic><topic>Image resolution</topic><topic>Layout</topic><topic>Pixel</topic><topic>Quadratic programming</topic><topic>Reflectivity</topic><topic>Remote sensing</topic><topic>Spatial resolution</topic><topic>Superresolution</topic><topic>Supervised classification</topic><topic>Target recognition</topic><toplevel>online_resources</toplevel><creatorcontrib>Mianji, F.A.</creatorcontrib><creatorcontrib>Ye Zhang</creatorcontrib><creatorcontrib>Hosseinipanah, M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Mianji, F.A.</au><au>Ye Zhang</au><au>Hosseinipanah, M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A classification enhancement in hyperspectral imagery using superresolution technique</atitle><btitle>2008 9th International Conference on Signal Processing</btitle><stitle>ICOSP</stitle><date>2008-10</date><risdate>2008</risdate><spage>998</spage><epage>1001</epage><pages>998-1001</pages><issn>2164-5221</issn><isbn>1424421780</isbn><isbn>9781424421787</isbn><eisbn>1424421799</eisbn><eisbn>9781424421794</eisbn><abstract>In this paper an improved supervised classification technique through applying a superresolution method on remotely sensed hyperspectral images is introduced. Superresolution methods provide high-resolution images from a sequence of low-resolution frames. In the proposed technique, classification of the hyperspectral image is carried out using spectrally homogenous training classes of pixels. Low spatial resolution frames of different wavelengths of the hyperspectral image are fed to a quadratic programming based classification algorithm to enhance the spatial resolution of the classification process. The results show a better classification and an edge improvement. Target recognition is the main field which can benefit from this technique.</abstract><pub>IEEE</pub><doi>10.1109/ICOSP.2008.4697296</doi><tpages>4</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Hyperspectral imagery Hyperspectral imaging Hyperspectral sensors Image resolution Layout Pixel Quadratic programming Reflectivity Remote sensing Spatial resolution Superresolution Supervised classification Target recognition |
| title | A classification enhancement in hyperspectral imagery using superresolution technique |
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