August 14, 2020 Dataset Open Access

Michał Romaszewski; Przemysław Głomb; Arkadiusz Sochan; Michał Cholewa

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  <identifier identifierType="DOI">10.5281/zenodo.3984905</identifier>
  <creators>
    <creator>
      <creatorName>Michał Romaszewski</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-8227-929X</nameIdentifier>
      <affiliation>ITAI PAS</affiliation>
    </creator>
    <creator>
      <creatorName>Przemysław Głomb</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-0215-4674</nameIdentifier>
      <affiliation>ITAI PAS</affiliation>
    </creator>
    <creator>
      <creatorName>Arkadiusz Sochan</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-5803-0278</nameIdentifier>
      <affiliation>ITAI PAS</affiliation>
    </creator>
    <creator>
      <creatorName>Michał Cholewa</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-6549-1590</nameIdentifier>
      <affiliation>ITAI PAS</affiliation>
    </creator>
  </creators>
  <titles>
    <title>A Dataset for Evaluating Blood Detection in Hyperspectral Images</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>Hyperspectral Imaging, Machine Learning, blood detection, classification, target detection</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-08-14</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3984905</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="arXiv" relationType="Cites" resourceTypeGeneral="Preprint">arXiv:2008.10254</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="References" resourceTypeGeneral="Software">https://github.com/iitis/HSI_blood_detection</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsDocumentedBy" resourceTypeGeneral="JournalArticle">10.1016/j.forsciint.2021.110701</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsCitedBy" resourceTypeGeneral="JournalArticle">10.3390/s20226666</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3984904</relatedIdentifier>
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  <version>1.0</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;The sensitivity of hyperspectral imaging (imaging spectroscopy) to haemoglobin derivatives makes it a promising tool for detection and classification of blood. However, due to complexity and high dimensionality of hyperspectral images, the development of hyperspectral blood detection algorithms is challenging. To facilitate their development, we present a new hyperspectral blood detection dataset. This dataset consists of 14 hyperspectral images (ENVI format) of a mock-up scene containing blood and visually similar substances (e.g. artificial blood or tomato concentrate). Images were taken over a period of three weeks and differ in terms of background composition and lighting intensity. To facilitate the use of data, the dataset includes an annotation of classes: pixels where blood and similar substances are visible have been marked by the authors. The main intention behind the dataset is to serve as testing data for Machine Learning methods for hyperspectral target detection and classification.&lt;/p&gt;</description>
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