Cloud detection in all-sky images via multi-scale neighborhood features and multiple supervised learning techniques

Cheng, Hsu-Yung; Lin, Chih-Lung

doi:https://doi.org/10.5194/amt-10-199-2017

Articles | Volume 10, issue 1

https://doi.org/10.5194/amt-10-199-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/amt-10-199-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 10, issue 1

Research article

|

17 Jan 2017

Research article |

| 17 Jan 2017

Cloud detection in all-sky images via multi-scale neighborhood features and multiple supervised learning techniques

Hsu-Yung Cheng and Chih-Lung Lin

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Final revised paper (published on 17 Jan 2017)
Preprint (discussion started on 09 Aug 2016)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review of the manuscript “Cloud Detection in All-Sky Images via Multi-scale Neighborhood Features and Multiple Supervised Learning Techniques” by Hsu-Yung Cheng and Chih-Lung Lin.', Anonymous Referee #2, 12 Sep 2016
- AC1: 'Revision in preparation', Hsu-Yung Cheng, 30 Sep 2016
  - AC2: 'Author's response', Hsu-Yung Cheng, 27 Oct 2016
    - AC3: 'Revised manuscript', Hsu-Yung Cheng, 27 Oct 2016
RC2: 'Review of manuscript', Jessica Kleiss, 12 Nov 2016
- AC4: 'Author's response', Hsu-Yung Cheng, 01 Dec 2016
- AC5: 'Revised manuscript', Hsu-Yung Cheng, 01 Dec 2016

Short summary

A cloud detection method for all-sky images is proposed. Obtaining improved cloud detection results is helpful for cloud classification, tracking and solar irradiance prediction. The features are extracted from local image patches with different sizes to include local structure and multi-resolution information. The cloud models are learned through the training process. We have shown that taking advantages of multiple classifiers and various patch sizes is able to increase the detection accuracy.