Using Hurst and Lyapunov Exponent For Hyperspectral Image Feature Extraction
Hyperspectral image processing has attracted high attention in remote sensing fields. One of the main issues is to develop efficient methods for dimensionality reduction via feature extraction. This letter proposes a new nonlinear unsupervised feature extraction algorithm using Hurst and Lyapunov ex...
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| Veröffentlicht in: | IEEE geoscience and remote sensing letters 2012-07, Vol.9 (4), p.705-709 |
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| container_title | IEEE geoscience and remote sensing letters |
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| creator | Yin, Jihao Gao, Chao Jia, Xiuping |
| description | Hyperspectral image processing has attracted high attention in remote sensing fields. One of the main issues is to develop efficient methods for dimensionality reduction via feature extraction. This letter proposes a new nonlinear unsupervised feature extraction algorithm using Hurst and Lyapunov exponents to reveal local and general spectral profiles, respectively. A hyperspectral reflectance curve from each pixel is regarded as a time series, and it is represented by Hurst and Lyapunov exponents. These two new features are then used to overcome the Hughes problem for reliable classification. Experimental results show that the proposed method performs better than a few other feature extraction methods tested. |
| doi_str_mv | 10.1109/LGRS.2011.2179005 |
| format | Article |
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One of the main issues is to develop efficient methods for dimensionality reduction via feature extraction. This letter proposes a new nonlinear unsupervised feature extraction algorithm using Hurst and Lyapunov exponents to reveal local and general spectral profiles, respectively. A hyperspectral reflectance curve from each pixel is regarded as a time series, and it is represented by Hurst and Lyapunov exponents. These two new features are then used to overcome the Hughes problem for reliable classification. Experimental results show that the proposed method performs better than a few other feature extraction methods tested.</description><identifier>ISSN: 1545-598X</identifier><identifier>EISSN: 1558-0571</identifier><identifier>DOI: 10.1109/LGRS.2011.2179005</identifier><identifier>CODEN: IGRSBY</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Accuracy ; Classification ; Feature extraction ; Hurst exponent ; hyperspectral image ; Hyperspectral imaging ; Lyapunov exponent ; Lyapunov exponents ; Principal component analysis ; Reflectance curves ; Remote sensing ; Spectra ; Time series ; Time series analysis</subject><ispartof>IEEE geoscience and remote sensing letters, 2012-07, Vol.9 (4), p.705-709</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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One of the main issues is to develop efficient methods for dimensionality reduction via feature extraction. This letter proposes a new nonlinear unsupervised feature extraction algorithm using Hurst and Lyapunov exponents to reveal local and general spectral profiles, respectively. A hyperspectral reflectance curve from each pixel is regarded as a time series, and it is represented by Hurst and Lyapunov exponents. These two new features are then used to overcome the Hughes problem for reliable classification. Experimental results show that the proposed method performs better than a few other feature extraction methods tested.</description><subject>Accuracy</subject><subject>Classification</subject><subject>Feature extraction</subject><subject>Hurst exponent</subject><subject>hyperspectral image</subject><subject>Hyperspectral imaging</subject><subject>Lyapunov exponent</subject><subject>Lyapunov exponents</subject><subject>Principal component analysis</subject><subject>Reflectance curves</subject><subject>Remote sensing</subject><subject>Spectra</subject><subject>Time series</subject><subject>Time series analysis</subject><issn>1545-598X</issn><issn>1558-0571</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkE1Lw0AQhoMoWKs_QLwsePGSurObTTZHKf2CgKAWvIXtdrakpNm4m4j9925o8eBphpfnHYYniu6BTgBo_lws3t4njAJMGGQ5peIiGoEQMqYig8thT0Qscvl5Hd14v6eUJVJmo6hY-6rZkWXvfEdUsyXFUbV9Y7_J7Ke1DTYdmVtHlscWnW9Rd07VZHVQOyRzVF3vMIAh1F1lm9voyqja4915jqP1fPYxXcbF62I1fSlizVnaxSZJNwolaMWNNik3OWUsBaG12QDfgMpNQgGN3BpItzLVmmNIBIeEZzQTfBw9ne62zn716LvyUHmNda0atL0vgXLglELGAvr4D93b3jXhu0ABE1yIVAYKTpR21nuHpmxddVDuGKBy8FsOfsvBb3n2GzoPp06FiH98ClwymfNf8EV2Lg</recordid><startdate>20120701</startdate><enddate>20120701</enddate><creator>Yin, Jihao</creator><creator>Gao, Chao</creator><creator>Jia, Xiuping</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>20120701</creationdate><title>Using Hurst and Lyapunov Exponent For Hyperspectral Image Feature Extraction</title><author>Yin, Jihao ; Gao, Chao ; Jia, Xiuping</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c326t-f46bae81ca3fcf63f9022615ccfb13b1a9f401ef8df16d86cc3ef405314370753</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Accuracy</topic><topic>Classification</topic><topic>Feature extraction</topic><topic>Hurst exponent</topic><topic>hyperspectral image</topic><topic>Hyperspectral imaging</topic><topic>Lyapunov exponent</topic><topic>Lyapunov exponents</topic><topic>Principal component analysis</topic><topic>Reflectance curves</topic><topic>Remote sensing</topic><topic>Spectra</topic><topic>Time series</topic><topic>Time series analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yin, Jihao</creatorcontrib><creatorcontrib>Gao, Chao</creatorcontrib><creatorcontrib>Jia, Xiuping</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>Yin, Jihao</au><au>Gao, Chao</au><au>Jia, Xiuping</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using Hurst and Lyapunov Exponent For Hyperspectral Image Feature Extraction</atitle><jtitle>IEEE geoscience and remote sensing letters</jtitle><stitle>LGRS</stitle><date>2012-07-01</date><risdate>2012</risdate><volume>9</volume><issue>4</issue><spage>705</spage><epage>709</epage><pages>705-709</pages><issn>1545-598X</issn><eissn>1558-0571</eissn><coden>IGRSBY</coden><abstract>Hyperspectral image processing has attracted high attention in remote sensing fields. 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| ispartof | IEEE geoscience and remote sensing letters, 2012-07, Vol.9 (4), p.705-709 |
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| source | IEEE Electronic Library (IEL) |
| subjects | Accuracy Classification Feature extraction Hurst exponent hyperspectral image Hyperspectral imaging Lyapunov exponent Lyapunov exponents Principal component analysis Reflectance curves Remote sensing Spectra Time series Time series analysis |
| title | Using Hurst and Lyapunov Exponent For Hyperspectral Image Feature Extraction |
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