Representation Learning for Cloud Classification

@inproceedings{Bernecker2013RepresentationLF,
  title={Representation Learning for Cloud Classification},
  author={David Bernecker and Christian Riess and Vincent Christlein and Elli Angelopoulou and Joachim Hornegger},
  booktitle={GCPR},
  year={2013}
}
Proper cloud segmentation can serve as an important precursor to predicting the output of solar power plants. However, due to the high variability of cloud appearance, and the high dynamic range between different sky regions, cloud segmentation is a surprisingly difficult task. In this paper, we present an approach to cloud segmentation and classification that is based on representation learning. Texture primitives of cloud regions are represented within a restricted Boltzmann Machine… CONTINUE READING

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Key Quantitative Results

  • Experimental results yield a relative improvement of the unweighted average (pixelwise) precision on a three-class problem by 11% to 94% in comparison to prior work.

References

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