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.
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.
the Creative Commons Attribution 3.0 License.
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
CORRESPONDING AUTHOR
Department of Computer Science and Information Engineering, National Central University, No. 300 Jhongda Rd., Jhongli City, Taoyuan 32001, Taiwan
Chih-Lung Lin
Department of Electronic Engineering, Hwa Hsia University of Technology, New Taipei City, Taiwan
Related authors
H.-Y. Cheng and C.-C. Yu
Atmos. Meas. Tech., 8, 1173–1182, https://doi.org/10.5194/amt-8-1173-2015, https://doi.org/10.5194/amt-8-1173-2015, 2015
Short summary
Short summary
This work performs cloud classification on all-sky images. To deal with mixed cloud types, we propose performing block-based classification. The proposed method combines local texture features with classical statistical texture features. The experimental results have shown that applying the combined feature results in higher classification accuracy. It is also validated that using block-based classification outperforms classification on the entire images.
H.-Y. Cheng and C.-C. Yu
Atmos. Meas. Tech., 8, 1173–1182, https://doi.org/10.5194/amt-8-1173-2015, https://doi.org/10.5194/amt-8-1173-2015, 2015
Short summary
Short summary
This work performs cloud classification on all-sky images. To deal with mixed cloud types, we propose performing block-based classification. The proposed method combines local texture features with classical statistical texture features. The experimental results have shown that applying the combined feature results in higher classification accuracy. It is also validated that using block-based classification outperforms classification on the entire images.
Related subject area
Subject: Clouds | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
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Bu-Yo Kim, Joo Wan Cha, and Ki-Ho Chang
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Ilona Ylivinkka, Santeri Kaupinmäki, Meri Virman, Maija Peltola, Ditte Taipale, Tuukka Petäjä, Veli-Matti Kerminen, Markku Kulmala, and Ekaterina Ezhova
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Carola Barrientos Velasco, Hartwig Deneke, Hannes Griesche, Patric Seifert, Ronny Engelmann, and Andreas Macke
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Christine Aebi, Julian Gröbner, Niklaus Kämpfer, and Laurent Vuilleumier
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Emmanuel Fontaine, Delphine Leroy, Alfons Schwarzenboeck, Julien Delanoë, Alain Protat, Fabien Dezitter, Alice Grandin, John Walter Strapp, and Lyle Edward Lilie
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Qingyong Li, Zhen Zhang, Weitao Lu, Jun Yang, Ying Ma, and Wen Yao
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Som Sharma, Rajesh Vaishnav, Munn V. Shukla, Prashant Kumar, Prateek Kumar, Pradeep K. Thapliyal, Shyam Lal, and Yashwant B. Acharya
Atmos. Meas. Tech., 9, 711–719, https://doi.org/10.5194/amt-9-711-2016, https://doi.org/10.5194/amt-9-711-2016, 2016
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Cloud base height observations from Ceilometer CL31 were extensively studied during May 2013 to January 2015 over Ahmedabad (23.03°N, 72.54°E), India. Results indicate that the ceilometer is an excellent instrument to precisely detect low- and mid-level clouds, and that the MODIS satellite provides accurate retrieval of high-level clouds over this region.
S. Gagné, L. P. MacDonald, W. R. Leaitch, and J. R. Pierce
Atmos. Meas. Tech., 9, 619–630, https://doi.org/10.5194/amt-9-619-2016, https://doi.org/10.5194/amt-9-619-2016, 2016
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Measurements of clouds with an aircraft are essential to understand how clouds form and how they affect the Earth's climate. These measurements are used in climate models to help predict how our climate might develop in the next century. Aircraft measurements are, however, difficult for modellers to interpret because the way they were acquired and analyzed varies from one team of scientists to the next. We present a software platform for scientists to share and compare their analysis tools.
H.-Y. Cheng and C.-C. Yu
Atmos. Meas. Tech., 8, 1173–1182, https://doi.org/10.5194/amt-8-1173-2015, https://doi.org/10.5194/amt-8-1173-2015, 2015
Short summary
Short summary
This work performs cloud classification on all-sky images. To deal with mixed cloud types, we propose performing block-based classification. The proposed method combines local texture features with classical statistical texture features. The experimental results have shown that applying the combined feature results in higher classification accuracy. It is also validated that using block-based classification outperforms classification on the entire images.
A. Korolev and P. R. Field
Atmos. Meas. Tech., 8, 761–777, https://doi.org/10.5194/amt-8-761-2015, https://doi.org/10.5194/amt-8-761-2015, 2015
M. L. Krüger, S. Mertes, T. Klimach, Y. F. Cheng, H. Su, J. Schneider, M. O. Andreae, U. Pöschl, and D. Rose
Atmos. Meas. Tech., 7, 2615–2629, https://doi.org/10.5194/amt-7-2615-2014, https://doi.org/10.5194/amt-7-2615-2014, 2014
M. I. A. Berghof, G. P. Frank, S. Sjogren, and B. G. Martinsson
Atmos. Meas. Tech., 7, 877–886, https://doi.org/10.5194/amt-7-877-2014, https://doi.org/10.5194/amt-7-877-2014, 2014
A. Schwarzenboeck, G. Mioche, A. Armetta, A. Herber, and J.-F. Gayet
Atmos. Meas. Tech., 2, 779–788, https://doi.org/10.5194/amt-2-779-2009, https://doi.org/10.5194/amt-2-779-2009, 2009
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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.
A cloud detection method for all-sky images is proposed. Obtaining improved cloud detection...
