April 7, 2025 Dataset Open Access

Arbash, Elias; Afifi, Ahmed Jamal Mohammaed; Belahsen, Ymane; Fuchs, Margret; Ghamisi, Pedram; Scheunders, Paul; Gloaguen, Richard

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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Arbash, Elias</dc:creator>
  <dc:creator>Afifi, Ahmed Jamal Mohammaed</dc:creator>
  <dc:creator>Belahsen, Ymane</dc:creator>
  <dc:creator>Fuchs, Margret</dc:creator>
  <dc:creator>Ghamisi, Pedram</dc:creator>
  <dc:creator>Scheunders, Paul</dc:creator>
  <dc:creator>Gloaguen, Richard</dc:creator>
  <dc:date>2025-04-07</dc:date>
  <dc:description>Electrolyzers-HSI Dataset&#13;
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Description:&#13;
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The Electrolyzers-HSI dataset is a multiscene RGB-Hyperspectral benchmark dataset comprising 55 scene of shredded Electrolyzers samples. The RGB images are collected using a Teledyne Dalsa C4020 camera on a conveyor belt, while hyperspectral images (HSI) are acquired with a FENIX spectrometer. The HSI data contains 450 bands in the VNIR and SWIR range [400 - 2500]nm.&#13;
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Data Format&#13;
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 RGB Images: .jpg files&#13;
 Ground Truth (GT): .png files. They appear black since the values are between 0 and 5. Correct visualization is done via script.&#13;
 HSI Data: Each hyperspectral data cube .img file is accompanied by a .hdr file.&#13;
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Folder Organization&#13;
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 Electrolyzers-HSI: 55 subfolders &#13;
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  1/&#13;
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   ’GT.png’ file for segmentation ground truth&#13;
   ‘HSI.img’ and ‘HSI.hdr’ files for HSI data cube&#13;
   ‘RGB.jpg’ file for the RGB image&#13;
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  2/&#13;
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   ’GT.png’ file for segmentation ground truth&#13;
   ‘HSI.img’ and ‘HSI.hdr’ files for HSI data cube&#13;
   ‘RGB.jpg’ file for the RGB image&#13;
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  3/4/5/6/ … :Same structure for all rest of folders&#13;
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Data Classes in Masks&#13;
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 Masks contain 0 to 5 segmentation classes:&#13;
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  0: background&#13;
  1: “MESH”&#13;
  2: “Steel_Cathode”&#13;
  3: "Steel_Anode”&#13;
  4: “HTEL_Anode”&#13;
  5: “HTEL_Cathode”&#13;
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Code Repository&#13;
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To facilitate reading and working with the data, Python codes are available on the GitHub repository:&#13;
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https://github.com/hifexplo&#13;
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Citation&#13;
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If you use this dataset, please cite the following article:&#13;
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Word:&#13;
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Latex:</dc:description>
  <dc:identifier>https://rodare.hzdr.de/record/3668</dc:identifier>
  <dc:identifier>10.14278/rodare.3668</dc:identifier>
  <dc:identifier>oai:rodare.hzdr.de:3668</dc:identifier>
  <dc:relation>url:https://www.hzdr.de/publications/Publ-41192</dc:relation>
  <dc:relation>doi:10.14278/rodare.3667</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/hzdr</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/rodare</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>circular economy</dc:subject>
  <dc:subject>automated data processing</dc:subject>
  <dc:subject>optical sensors</dc:subject>
  <dc:subject>Hyperspectral Imaging</dc:subject>
  <dc:subject>HSI</dc:subject>
  <dc:subject>Hyperspectral Imaging classification</dc:subject>
  <dc:subject>recycling</dc:subject>
  <dc:subject>E-waste</dc:subject>
  <dc:subject>hyperspectral imaging dataset</dc:subject>
  <dc:subject>RGB dataset</dc:subject>
  <dc:subject>conveyor belt</dc:subject>
  <dc:subject>sensors</dc:subject>
  <dc:subject>spectrometers</dc:subject>
  <dc:subject>machine learning</dc:subject>
  <dc:subject>deep learning</dc:subject>
  <dc:subject>Electrolyzers</dc:subject>
  <dc:subject>open source</dc:subject>
  <dc:subject>digitalization</dc:subject>
  <dc:subject>Transformers</dc:subject>
  <dc:title>Electrolyzers-HSI: Close-Range Multi-Scene Hyperspectral Imaging Benchmark Dataset</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>