Hyperspectral soil texture classification
A soil texture classification system is developed and exploited in the hyperspectral domain. The hyperspectral signatures of three different pure soil textures, i.e., sand, silt and clay, combined with a linear mixture model, are used to generate signals representing different types of soil textures...
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| creator | Xudong Zhang Vijayaraj, V. Younan, N.H. |
| description | A soil texture classification system is developed and exploited in the hyperspectral domain. The hyperspectral signatures of three different pure soil textures, i.e., sand, silt and clay, combined with a linear mixture model, are used to generate signals representing different types of soil textures. Feature extraction via the discrete wavelet transform and linear discriminant analysis for feature vector reduction and optimization are used. Different types of classifiers, which include the nearest mean and maximum likelihood, are incorporated to test the system's applicability. Classification accuracy is evaluated using a leave-one-out method. Experimental results are presented and possible future works are discussed. |
| doi_str_mv | 10.1109/WARSD.2003.1295191 |
| format | Conference Proceeding |
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The hyperspectral signatures of three different pure soil textures, i.e., sand, silt and clay, combined with a linear mixture model, are used to generate signals representing different types of soil textures. Feature extraction via the discrete wavelet transform and linear discriminant analysis for feature vector reduction and optimization are used. Different types of classifiers, which include the nearest mean and maximum likelihood, are incorporated to test the system's applicability. Classification accuracy is evaluated using a leave-one-out method. 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The hyperspectral signatures of three different pure soil textures, i.e., sand, silt and clay, combined with a linear mixture model, are used to generate signals representing different types of soil textures. Feature extraction via the discrete wavelet transform and linear discriminant analysis for feature vector reduction and optimization are used. Different types of classifiers, which include the nearest mean and maximum likelihood, are incorporated to test the system's applicability. Classification accuracy is evaluated using a leave-one-out method. Experimental results are presented and possible future works are discussed.</description><subject>Discrete wavelet transforms</subject><subject>Feature extraction</subject><subject>Hyperspectral imaging</subject><subject>Hyperspectral sensors</subject><subject>Linear discriminant analysis</subject><subject>Sensor phenomena and characterization</subject><subject>Signal generators</subject><subject>Soil texture</subject><subject>Vectors</subject><subject>Wavelet analysis</subject><isbn>0780383508</isbn><isbn>9780780383500</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2003</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj0tLAzEURgMiqLV_QDezdTHjTW6TmyxLfVQoCD5wWe5kEoiMdkgi2H9vwX6bszuHT4grCZ2U4G4_li-vd50CwE4qp6WTJ-ICyAJa1GDPxLyUTzgMHWpU5-JmvZ9CLlPwNfPYlF0amxp-608OjR-5lBST55p235fiNPJYwvzImXh_uH9brdvN8-PTarlpkyRd2wX00tlDOjr0EokMDRQ5WmUUDNF7HQyZ3rJmHobgvSIgxb3mHsgsAGfi-t-bQgjbKacvzvvt8Q3-AVvQQFA</recordid><startdate>2003</startdate><enddate>2003</enddate><creator>Xudong Zhang</creator><creator>Vijayaraj, V.</creator><creator>Younan, N.H.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2003</creationdate><title>Hyperspectral soil texture classification</title><author>Xudong Zhang ; Vijayaraj, V. ; Younan, N.H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-40b198951f93c137767d7faf82620dfcc5e676b8a5aaddecc27072ab5ab076403</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Discrete wavelet transforms</topic><topic>Feature extraction</topic><topic>Hyperspectral imaging</topic><topic>Hyperspectral sensors</topic><topic>Linear discriminant analysis</topic><topic>Sensor phenomena and characterization</topic><topic>Signal generators</topic><topic>Soil texture</topic><topic>Vectors</topic><topic>Wavelet analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Xudong Zhang</creatorcontrib><creatorcontrib>Vijayaraj, V.</creatorcontrib><creatorcontrib>Younan, N.H.</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>Xudong Zhang</au><au>Vijayaraj, V.</au><au>Younan, N.H.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Hyperspectral soil texture classification</atitle><btitle>IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003</btitle><stitle>WARSD</stitle><date>2003</date><risdate>2003</risdate><spage>182</spage><epage>186</epage><pages>182-186</pages><isbn>0780383508</isbn><isbn>9780780383500</isbn><abstract>A soil texture classification system is developed and exploited in the hyperspectral domain. The hyperspectral signatures of three different pure soil textures, i.e., sand, silt and clay, combined with a linear mixture model, are used to generate signals representing different types of soil textures. Feature extraction via the discrete wavelet transform and linear discriminant analysis for feature vector reduction and optimization are used. Different types of classifiers, which include the nearest mean and maximum likelihood, are incorporated to test the system's applicability. Classification accuracy is evaluated using a leave-one-out method. Experimental results are presented and possible future works are discussed.</abstract><pub>IEEE</pub><doi>10.1109/WARSD.2003.1295191</doi><tpages>5</tpages></addata></record> |
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| recordid | cdi_ieee_primary_1295191 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Discrete wavelet transforms Feature extraction Hyperspectral imaging Hyperspectral sensors Linear discriminant analysis Sensor phenomena and characterization Signal generators Soil texture Vectors Wavelet analysis |
| title | Hyperspectral soil texture classification |
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