1 Ocak 2018 Konferans bildirisi Açık Erişim

Nar, Fatih; Yilmaz, Erdal; Camps-Valls, Gustau

DataCite XML

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/29071</identifier>
  <creators>
    <creator>
      <creatorName>Nar, Fatih</creatorName>
      <givenName>Fatih</givenName>
      <familyName>Nar</familyName>
      <affiliation>Konya Food &amp; Agr Univ, Konya, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Yilmaz, Erdal</creatorName>
      <givenName>Erdal</givenName>
      <familyName>Yilmaz</familyName>
      <affiliation>Zibumi Studios, Ankara, Turkey</affiliation>
    </creator>
    <creator>
      <creatorName>Camps-Valls, Gustau</creatorName>
      <givenName>Gustau</givenName>
      <familyName>Camps-Valls</familyName>
      <affiliation>Univ Valencia, IPL, Valencia, Spain</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Sparsity-Driven Digital Terrain Model Extraction</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2018</publicationYear>
  <dates>
    <date dateType="Issued">2018-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/29071</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.81043/aperta.29070</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.81043/aperta.29071</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">We here introduce an automatic Digital Terrain Model (DTM) extraction method. The proposed sparsity-driven DTM extractor (SD-DTM) takes a high-resolution Digital Surface Model (DSM) as an input and constructs a high-resolution DTM using the variational framework. To obtain an accurate DTM, an iterative approach is proposed for the minimization of the target variational cost function. Accuracy of the SD-DTM is shown in a real-world DSM data set. We show the efficiency and effectiveness of the approach both visually and quantitatively via residual plots in illustrative terrain types.</description>
  </descriptions>
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