Decorrelation-stretch based cloud detection for total sky images

Zhao, M; Zhang, C; Zhang, W; Li, W; Zhang, J

Decorrelation-stretch based cloud detection for total sky images

Zhao, M Zhang, C Zhang, W Li, W Zhang, J

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Publication Type:: Conference Proceeding
Citation:: 2015 Visual Communications and Image Processing, VCIP 2015, 2015
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Zhao, M	en_US
dc.contributor.author	Zhang, C	en_US
dc.contributor.author	Zhang, W	en_US
dc.contributor.author	Li, W	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-7240-3541	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	2015 Visual Communications and Image Processing, VCIP 2015, 2015	en_US
dc.identifier.isbn	9781467373142	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120871
dc.description.abstract	© 2015 IEEE. Cloud detection plays an important role in total-sky images based solar forecasting and has received more attention in recent years. Accurate cloud detection for complicated total-sky images is especially changeling due to the low contrast and vague boundaries between cloud and sky regions. Unlike the existing cloud detection method without any preprocessing, one novel decorrelation-stretch (DS) based method is proposed in this work, where the total-sky images are preprocessed using the DS algorithm firstly. With this enhancement, color feature disparity of cloud and sky can be intensified notably, and then a more accurate threshold can be obtained by applying the Minimum Cross Entropy (MCE) to the preprocessed image. Experimental results demonstrated the proposed scheme achieves better performance than the existing cloud detection methods on total-sky images, especially for images with low contrast or vague boundaries between cloud and sky regions.	en_US
dc.relation.ispartof	2015 Visual Communications and Image Processing, VCIP 2015	en_US
dc.relation.isbasedon	10.1109/VCIP.2015.7457900	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Decorrelation-stretch based cloud detection for total sky images	en_US
dc.type	Conference Proceeding
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080110 Simulation and Modelling	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2015 IEEE. Cloud detection plays an important role in total-sky images based solar forecasting and has received more attention in recent years. Accurate cloud detection for complicated total-sky images is especially changeling due to the low contrast and vague boundaries between cloud and sky regions. Unlike the existing cloud detection method without any preprocessing, one novel decorrelation-stretch (DS) based method is proposed in this work, where the total-sky images are preprocessed using the DS algorithm firstly. With this enhancement, color feature disparity of cloud and sky can be intensified notably, and then a more accurate threshold can be obtained by applying the Minimum Cross Entropy (MCE) to the preprocessed image. Experimental results demonstrated the proposed scheme achieves better performance than the existing cloud detection methods on total-sky images, especially for images with low contrast or vague boundaries between cloud and sky regions.

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http://hdl.handle.net/10453/120871