1 Ocak 2019 Konferans bildirisi Açık Erişim

Kahraman, Sevcan; Bacher, Raphael; Uezato, Tatsumi; Chanussot, Jocelyn; Tangel, Ali

DataCite XML

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/70065</identifier>
  <creators>
    <creator>
      <creatorName>Kahraman, Sevcan</creatorName>
      <givenName>Sevcan</givenName>
      <familyName>Kahraman</familyName>
      <affiliation>Kocaeli Univ Elect &amp; Telecommun Engn, TR-41100 Kocaeli, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Bacher, Raphael</creatorName>
      <givenName>Raphael</givenName>
      <familyName>Bacher</familyName>
      <affiliation>Univ Grenoble Alpes, CNRS, Grenoble INP, GIPSA Lab, F-38000 Grenoble, France</affiliation>
    </creator>
    <creator>
      <creatorName>Uezato, Tatsumi</creatorName>
      <givenName>Tatsumi</givenName>
      <familyName>Uezato</familyName>
      <affiliation>RIKEN, AIP, Tokyo, Japan</affiliation>
    </creator>
    <creator>
      <creatorName>Chanussot, Jocelyn</creatorName>
      <givenName>Jocelyn</givenName>
      <familyName>Chanussot</familyName>
      <affiliation>Univ Grenoble Alpes, CNRS, Grenoble INP, GIPSA Lab, F-38000 Grenoble, France</affiliation>
    </creator>
    <creator>
      <creatorName>Tangel, Ali</creatorName>
      <givenName>Ali</givenName>
      <familyName>Tangel</familyName>
      <affiliation>Kocaeli Univ Elect &amp; Telecommun Engn, TR-41100 Kocaeli, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Lidar-Guided Reduction Of Spectral Variability In Hyperspectral Imagery</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2019</publicationYear>
  <dates>
    <date dateType="Issued">2019-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/70065</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.81043/aperta.70064</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.81043/aperta.70065</relatedIdentifier>
  </relatedIdentifiers>
  <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 unmixing has attained a great importance in recent decades in remote sensing applications. Due to some external effect (illumination conditions) or internal effects (concentration of chlorophyll), spectral variability exists in hyperspectral images. This spectral variability causes significant errors in abundance estimates. In this paper, we propose a new framework that incorporates feature extraction with Digital Surface Model (DSM) clustering informationto suppress the effect of spectral variability in hyperspectral unmixing. In this way, meaningful material abundance estimates are obtained. Experiments are conducted on simulated data. Results show that spectral variability can be reduced with the aid of LiDAR data in hyperspectal unmixing.</description>
  </descriptions>
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