Assessing Cloud Segmentation in the Chromacity Diagram of All-Sky Images
All-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 teles...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/11/1902 |
| _version_ | 1797565599790923776 |
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| author | Lukáš Krauz Petr Janout Martin Blažek Petr Páta |
| author_facet | Lukáš Krauz Petr Janout Martin Blažek Petr Páta |
| author_sort | Lukáš Krauz |
| collection | DOAJ |
| description | All-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. |
| first_indexed | 2024-03-10T19:14:29Z |
| format | Article |
| id | doaj.art-d92718016eef4074b5b275689e0a8ae7 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T19:14:29Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d92718016eef4074b5b275689e0a8ae72023-11-20T03:33:17ZengMDPI AGRemote Sensing2072-42922020-06-011211190210.3390/rs12111902Assessing Cloud Segmentation in the Chromacity Diagram of All-Sky ImagesLukáš Krauz0Petr Janout1Martin Blažek2Petr Páta3Department of Radioelectronics, Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 166 27 Prague 6, Czech RepublicDepartment of Radioelectronics, Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 166 27 Prague 6, Czech RepublicInstituto de Astrofísica de Andalucía, CSIC, Glorieta de la Astronomía s/n, 18008 Granada, SpainDepartment of Radioelectronics, Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 166 27 Prague 6, Czech RepublicAll-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.https://www.mdpi.com/2072-4292/12/11/1902WILLIAMall-sky imagesground-basedWILLIAM Meteo Databasek-means++cloud segmentation |
| spellingShingle | Lukáš Krauz Petr Janout Martin Blažek Petr Páta Assessing Cloud Segmentation in the Chromacity Diagram of All-Sky Images Remote Sensing WILLIAM all-sky images ground-based WILLIAM Meteo Database k-means++ cloud segmentation |
| title | Assessing Cloud Segmentation in the Chromacity Diagram of All-Sky Images |
| title_full | Assessing Cloud Segmentation in the Chromacity Diagram of All-Sky Images |
| title_fullStr | Assessing Cloud Segmentation in the Chromacity Diagram of All-Sky Images |
| title_full_unstemmed | Assessing Cloud Segmentation in the Chromacity Diagram of All-Sky Images |
| title_short | Assessing Cloud Segmentation in the Chromacity Diagram of All-Sky Images |
| title_sort | assessing cloud segmentation in the chromacity diagram of all sky images |
| topic | WILLIAM all-sky images ground-based WILLIAM Meteo Database k-means++ cloud segmentation |
| url | https://www.mdpi.com/2072-4292/12/11/1902 |
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