1 Ocak 2019 Konferans bildirisi Açık Erişim

Kahraman, Sevcan; Xu, Yang; Chanussot, Jocelyn; Tangel, Ali

DataCite XML

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/70067</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>Xu, Yang</creatorName>
      <givenName>Yang</givenName>
      <familyName>Xu</familyName>
      <affiliation>Nanjing Univ Sci &amp; Technol, Sch Comp Sci &amp; Engn, Nanjing 210094, Peoples R China</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 Data-Aided Hypergraph Regularized Multi-Modal Unmixing</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/70067</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.81043/aperta.70066</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.81043/aperta.70067</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">In recent years, there have been many advances in sensor technology which provides more useful information about the observed scene. Two of the newest remote sensing technologies are hyperspectral (HS) and Light Detection And Ranging (LiDAR) sensors. Since pixels in a small spatial neighborhood are more likely to share similar abundances, hypergraph regularization (HG-NMF) can be employed to handle the similarity relevance among the spatial neighborhood pixels. In this paper, we provide a LiDAR data-aided HS unmixing using HG-NMF. The composite usage of all these valuable information can lead to higher accuracy unmixing results. The obtained convex optimization problem is solved by Spectral Unmixing by Split Augmented Lagrangian (SUnSAL-TV) Algorithm. Experiments on synthetic data are conducted. The advantage of HG-NMF regularization is also demonstrated.</description>
  </descriptions>
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