Spatial-spectral data fusion for resolution enhancement of hyperspectral imagery
A new spatial-spectral data fusion technique based on spectral mixture analysis and super-resolution mapping for spatial resolution enhancement of hyperspectral imagery is proposed in this paper. To this end, a linear mixture model and a constrained least squares based unmixing algorithm are applied...
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| creator | Mianji, F.A. Ye Zhang Yanfeng Gu Babakhani, A. |
| description | A new spatial-spectral data fusion technique based on spectral mixture analysis and super-resolution mapping for spatial resolution enhancement of hyperspectral imagery is proposed in this paper. To this end, a linear mixture model and a constrained least squares based unmixing algorithm are applied for spectral unmixing of the hyperspectral imagery and the resulted fractional images are processed based on a spatial-spectral information correlation model through a super-resolution mapping technique. The obtained results validate the effectiveness of the method. It doesn't need any a priori information of the scene or secondary high resolution source of data, and is fast. |
| doi_str_mv | 10.1109/IGARSS.2009.5417949 |
| format | Conference Proceeding |
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To this end, a linear mixture model and a constrained least squares based unmixing algorithm are applied for spectral unmixing of the hyperspectral imagery and the resulted fractional images are processed based on a spatial-spectral information correlation model through a super-resolution mapping technique. The obtained results validate the effectiveness of the method. 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To this end, a linear mixture model and a constrained least squares based unmixing algorithm are applied for spectral unmixing of the hyperspectral imagery and the resulted fractional images are processed based on a spatial-spectral information correlation model through a super-resolution mapping technique. The obtained results validate the effectiveness of the method. It doesn't need any a priori information of the scene or secondary high resolution source of data, and is fast.</description><subject>1f noise</subject><subject>Computational efficiency</subject><subject>data fusion</subject><subject>hyperspectral imagery</subject><subject>Hyperspectral imaging</subject><subject>Image resolution</subject><subject>Least squares methods</subject><subject>Libraries</subject><subject>Pixel</subject><subject>resolution enhancement</subject><subject>Spatial resolution</subject><subject>Spectral analysis</subject><subject>spectral unmixing</subject><subject>super-resolution mapping</subject><subject>Training data</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><sourceid>RIE</sourceid><recordid>eNpFUMtuwkAM3D6QCpQv4JIfCPVmH4mPCLUUCalVwx05wVtShSTahAN_3yCi1hfLM5qxZoSYS1hICfiyWS-_0nQRAeDCaBmjxjsxkTrSWik0eC_GkTQqjAHUwz-h4XEgLKIdiUlvkKCEHnoSs7b9gX60AavjsfhMG-oKKsO24bzzVAYH6ihw57aoq8DVPvDc1uW5u55cHanK-cRVF9QuOF4a9n-64kTf7C_PYuSobHk27KnYvb3uVu_h9mO9WS23YYHQhWjIxs5ynJkIM6VJZolDTCJNnGmH1yhA6HJjnFV5hpQYezAytw6lgVhNxfxmWzDzvvH9d3_ZDy2pX862Vps</recordid><startdate>200907</startdate><enddate>200907</enddate><creator>Mianji, F.A.</creator><creator>Ye Zhang</creator><creator>Yanfeng Gu</creator><creator>Babakhani, A.</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>Spatial-spectral data fusion for resolution enhancement of hyperspectral imagery</title><author>Mianji, F.A. ; Ye Zhang ; Yanfeng Gu ; Babakhani, A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-95a67f6e7b529b34a1b8f99824aeb4f942440a9fc55f63cb9a856d51c6f915073</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>1f noise</topic><topic>Computational efficiency</topic><topic>data fusion</topic><topic>hyperspectral imagery</topic><topic>Hyperspectral imaging</topic><topic>Image resolution</topic><topic>Least squares methods</topic><topic>Libraries</topic><topic>Pixel</topic><topic>resolution enhancement</topic><topic>Spatial resolution</topic><topic>Spectral analysis</topic><topic>spectral unmixing</topic><topic>super-resolution mapping</topic><topic>Training data</topic><toplevel>online_resources</toplevel><creatorcontrib>Mianji, F.A.</creatorcontrib><creatorcontrib>Ye Zhang</creatorcontrib><creatorcontrib>Yanfeng Gu</creatorcontrib><creatorcontrib>Babakhani, A.</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 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>Mianji, F.A.</au><au>Ye Zhang</au><au>Yanfeng Gu</au><au>Babakhani, A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Spatial-spectral data fusion for resolution enhancement of 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-1011</spage><epage>III-1014</epage><pages>III-1011-III-1014</pages><issn>2153-6996</issn><eissn>2153-7003</eissn><isbn>1424433940</isbn><isbn>9781424433940</isbn><eisbn>1424433959</eisbn><eisbn>9781424433957</eisbn><abstract>A new spatial-spectral data fusion technique based on spectral mixture analysis and super-resolution mapping for spatial resolution enhancement of hyperspectral imagery is proposed in this paper. To this end, a linear mixture model and a constrained least squares based unmixing algorithm are applied for spectral unmixing of the hyperspectral imagery and the resulted fractional images are processed based on a spatial-spectral information correlation model through a super-resolution mapping technique. The obtained results validate the effectiveness of the method. It doesn't need any a priori information of the scene or secondary high resolution source of data, and is fast.</abstract><pub>IEEE</pub><doi>10.1109/IGARSS.2009.5417949</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-1011-III-1014 |
| issn | 2153-6996 2153-7003 |
| language | eng |
| recordid | cdi_ieee_primary_5417949 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | 1f noise Computational efficiency data fusion hyperspectral imagery Hyperspectral imaging Image resolution Least squares methods Libraries Pixel resolution enhancement Spatial resolution Spectral analysis spectral unmixing super-resolution mapping Training data |
| title | Spatial-spectral data fusion for resolution enhancement of hyperspectral imagery |
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