Journal article Open Access

A Time-Weighted Dynamic Time Warping Method for Land-Use and Land-Cover Mapping

Maus, Victor; Camara, Gilberto; Souza, Ricardo; Sanchez, Alber; Ramos, Fernando

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      <creatorName>Maus, Victor</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0002-7385-4723</nameIdentifier>
      <affiliation>University of Vienna</affiliation>
      <creatorName>Camara, Gilberto</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0002-3681-487X</nameIdentifier>
      <affiliation>INPE (National Institute for Space Research), Brazil</affiliation>
      <creatorName>Souza, Ricardo</creatorName>
      <affiliation>INPE (National Institute for Space Research), Brazil</affiliation>
      <creatorName>Sanchez, Alber</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0001-7966-2880</nameIdentifier>
      <affiliation>INPE (National Institute for Space Research), Brazil</affiliation>
      <creatorName>Ramos, Fernando</creatorName>
      <affiliation>INPE (National Institute for Space Research), Brazil</affiliation>
    <title>A Time-Weighted Dynamic Time Warping Method for Land-Use and Land-Cover Mapping</title>
    <subject>time series analysis, agriculture, remote sensing time series, dynamic time warping</subject>
    <date dateType="Issued">2016-02-11</date>
  <resourceType resourceTypeGeneral="JournalArticle"/>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/JSTARS.2016.2517118</relatedIdentifier>
    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;This paper presents a time-weighted version of the dynamic time warping (DTW) method for land-use and land-cover classification using remote sensing image time series. Methods based on DTW have achieved significant results in time-series data mining. The original DTW method works well for shape matching, but is not suited for remote sensing time-series classification. It disregards the temporal range when finding the best alignment between two time series. Since each land-cover class has a specific phenological cycle, a good time-series land-cover classifier needs to balance between shape matching and temporal alignment. To that end, we adjusted the original DTW method to include a temporal weight that accounts for seasonality of land-cover types. The resulting algorithm improves on previous methods for land-cover classification using DTW. In a case study in a tropical forest area, our proposed logistic time-weighted version achieves the best overall accuracy of 87.32%. The accuracy of a version with maximum time delay constraints is 84.66%. A time-warping method without time constraints has a 70.14% accuracy. To get good results with the proposed algorithm, the spatial and temporal resolutions of the data should capture the properties of the landscape. The pattern samples should also represent well the temporal variation of land cover.&lt;/p&gt;</description>
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