Abstract
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. Quantitative results are encouraging. 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.
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Bernecker, D., Riess, C., Christlein, V., Angelopoulou, E., Hornegger, J. (2013). Representation Learning for Cloud Classification. In: Weickert, J., Hein, M., Schiele, B. (eds) Pattern Recognition. GCPR 2013. Lecture Notes in Computer Science, vol 8142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40602-7_42
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DOI: https://doi.org/10.1007/978-3-642-40602-7_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40601-0
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