1 Ocak 2013 Konferans bildirisi Açık Erişim

Kozal, Ali Omer; Teke, Mustafa; Ilgin, Hakki Alparslan

DataCite XML

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/91701</identifier>
  <creators>
    <creator>
      <creatorName>Kozal, Ali Omer</creatorName>
      <givenName>Ali Omer</givenName>
      <familyName>Kozal</familyName>
    </creator>
    <creator>
      <creatorName>Teke, Mustafa</creatorName>
      <givenName>Mustafa</givenName>
      <familyName>Teke</familyName>
      <affiliation>TUBITAK UZAY, Turkiye Bilimsel &amp; Teknol Arastirma Kurumu, Uzay Teknol Arastirma Enstitusu, ODTU Yerleskesi, TR-06531 Ankara, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Ilgin, Hakki Alparslan</creatorName>
      <givenName>Hakki Alparslan</givenName>
      <familyName>Ilgin</familyName>
      <affiliation>Ankara Univ, Elektrik &amp; Elektronik Muhendisligi, Ankara, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Comparative Analysis Of Hyperspectral Dimension Reduction Methods</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2013</publicationYear>
  <dates>
    <date dateType="Issued">2013-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/91701</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.81043/aperta.91700</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.81043/aperta.91701</relatedIdentifier>
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  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">Hyperspectral sensors generate images in narrow bands in continuous manner with hundreds of spectral bands. The data with large number of bands require more processing power to classify. To decrease the redundancy in hyperspectral images and increase classifying efficiency with less number of bands, dimension reduction techniques are applied. In this paper, linear and non-linear dimension reduction methods are compared in classification performance and calculation time.</description>
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