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Contrastive Learning approach for blind Hyperspectral Unmixing (CLHU)
  • +7
  • Abian Hernandez-Guedes ,
  • Ryodai Fukushima ,
  • Toshihiro Takamatsu ,
  • Himar Fabelo ,
  • samuel ortega ,
  • Nobuyoshi Takeshita ,
  • Hiro Hasegawa ,
  • Juan Ruiz-Alzola ,
  • Hiroshi Takemura ,
  • Gustavo M. Callico
Abian Hernandez-Guedes
University of Las Palmas de Gran Canaria

Corresponding Author:[email protected]

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Ryodai Fukushima
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Toshihiro Takamatsu
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Himar Fabelo
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samuel ortega
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Nobuyoshi Takeshita
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Hiro Hasegawa
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Juan Ruiz-Alzola
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Hiroshi Takemura
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Gustavo M. Callico
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The Contrastive Learning for blind Hyperspectral Unmixing (CLHU) is a self-supervised deep learning approach for blind hyperspectral unmixing. Unlike existing deep learning methods that rely on reconstruction capabilities, CLHU leverages the input-endmembers relationship for abundance estimation. The endmembers are dynamically updated during training, controlled by two regularization factors. Extensive experiments demonstrate CLHU’s effectiveness, achieving state-of-the-art performance in hyperspectral unmixing. This novel approach offers a promising perspective for the field and holds potential for further enhancements in hyperspectral unmixing tasks.
27 Feb 2024Submitted to TechRxiv
04 Mar 2024Published in TechRxiv