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dc.contributor.authorKrauz, Lukášes_ES
dc.contributor.authorJanout, Petres_ES
dc.contributor.authorBlažek, Martines_ES
dc.contributor.authorPáta, Petres_ES
dc.date.accessioned2020-07-16T10:15:10Z-
dc.date.available2020-07-16T10:15:10Z-
dc.date.issued2020-06-11-
dc.identifier.citationRemote Sensing 12(11): 1902 (2020)es_ES
dc.identifier.urihttp://hdl.handle.net/10261/216753-
dc.descriptionOpen Access.--This article belongs to the Special Issue Remote Sensing of Cloudses_ES
dc.description.abstractAll-sky imaging systems are currently very popular. They are used in ground-based meteorological stations and as a crucial part of the weather monitors for autonomous robotic telescopes. Data from all-sky imaging cameras provide important information for controlling meteorological stations and telescopes, and they have specific characteristics different from widely-used imaging systems. A particularly promising and useful application of all-sky cameras is for remote sensing of cloud cover. Post-processing of the image data obtained from all-sky imaging cameras for automatic cloud detection and for cloud classification is a very demanding task. Accurate and rapid cloud detection can provide a good way to forecast weather events such as torrential rainfalls. However, the algorithms that are used must be specifically calibrated on data from the all-sky camera in order to set up an automatic cloud detection system. This paper presents an assessment of a modified k-means++ color-based segmentation algorithm specifically adjusted to the WILLIAM (WIde-field aLL-sky Image Analyzing Monitoring system) ground-based remote all-sky imaging system for cloud detection. The segmentation method is assessed in two different color-spaces (L*a*b and XYZ). Moreover, the proposed algorithm is tested on our public WMD database (WILLIAM Meteo Database) of annotated all-sky image data, which was created specifically for testing purposes. The WMD database is available for public use. In this paper, we present a comparison of selected color-spaces and assess their suitability for the cloud color segmentation based on all-sky images. In addition, we investigate the distribution of the segmented cloud phenomena present on the all-sky images based on the color-spaces channels. In the last part of this work, we propose and discuss the possible exploitation of the color-based k-means++ segmentation method as a preprocessing step towards cloud classification in all-sky images. © 2020 by the authors.es_ES
dc.description.sponsorshipThis work was supported by the Grant Agency of the Czech Technical University in Prague, Grant No. SGS20/179/OHK3/3T/13, "Modern Optical Imaging Systems with Non-linear Point Spread Function and Advanced Algorithms for Image Data Processing", and by the Grant Agency of the Czech Republic, Grant No. 20-10907S, "Meteor clusters: An evidence for fragmentation of meteoroids in interplanetary space". Martin Blazek acknowledges funding under Fellowship Number PTA2016-13192-I and financial support from the State Agency for Research of the Spanish MCIUthrough the "Center of Excellence Severo Ochoa" award to the Instituto de Astrofisica de Andalucia (SEV-2017-0709).es_ES
dc.language.isoenges_ES
dc.publisherMultidisciplinary Digital Publishing Institutees_ES
dc.relation.isversionofPublisher's versiones_ES
dc.rightsopenAccesses_ES
dc.subjectWILLIAMes_ES
dc.subjectAll-sky imageses_ES
dc.subjectGround-basedes_ES
dc.subjectWILLIAM Meteo Databasees_ES
dc.subjectK-means pluses_ES
dc.subjectCloud segmentationes_ES
dc.subjectCloud detectiones_ES
dc.subjectCloud classificationes_ES
dc.titleAssessing Cloud Segmentation in the Chromacity Diagram of All-Sky Imageses_ES
dc.typeartículoes_ES
dc.identifier.doi10.3390/rs12111902-
dc.description.peerreviewedPeer reviewedes_ES
dc.relation.publisherversionhttp://dx.doi.org/10.3390/rs12111902es_ES
dc.identifier.e-issn2072-4292-
dc.contributor.funderCzech Grant Agencyes_ES
dc.contributor.funderCzech Technical University in Praguees_ES
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (España)es_ES
dc.contributor.funderEuropean Commissiones_ES
dc.relation.csices_ES
oprm.item.hasRevisionno ko 0 false*
dc.identifier.funderhttp://dx.doi.org/10.13039/501100000780es_ES
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