Unsupervised Segmentation of Hyperspectral Images Using 3-D Convolutional Autoencoders

@article{Nalepa2020UnsupervisedSO,
  title={Unsupervised Segmentation of Hyperspectral Images Using 3-D Convolutional Autoencoders},
  author={J. Nalepa and Michal Myller and Y. Imai and K. Honda and T. Takeda and Marek Antoniak},
  journal={IEEE Geoscience and Remote Sensing Letters},
  year={2020},
  volume={17},
  pages={1948-1952}
}
  • J. Nalepa, Michal Myller, +3 authors Marek Antoniak
  • Published 2020
  • Computer Science
  • IEEE Geoscience and Remote Sensing Letters
  • Hyperspectral image analysis has become an important topic widely researched by the remote sensing community. [...] Key Method We introduce a new deep architecture which couples 3D convolutional autoencoders with clustering. Our multi-faceted experimental study---performed over benchmark and real-life data---revealed that our approach delivers high-quality segmentation without any prior class labels.Expand Abstract
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