Abstract
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.
Highlights
Ground-based all-sky imaging systems generate a great amount of all-sky image data that are suitable for advanced image processing
This paper provides an evaluation of the modified daylight k-means++ color-based segmentation method adjusted for all-sky image data from the WILLIAM (WIde-field aLL-sky Image Analyzing Monitoring) system and presents an analysis of the cloud type formations within selected color-spaces
This paper presented an evaluation of the k-means++ cloud color-based segmentation algorithm based on all-sky image data from the WILLIAM ground-based whole sky imaging system
Summary
Ground-based all-sky imaging systems generate a great amount of all-sky image data that are suitable for advanced image processing. Using these data, a range of weather phenomena can be detected, analyzed, and classified. All-sky image data from ground-based systems have been exploited for various astronomical and weather evaluation purposes. All-sky image data are exploited for cloud observation, for cloud detection, and for cloud classification. Cloud color-based segmentation involves selecting a suitable color-space into which the image data are transformed, followed by evaluations of the accuracy of the segmentation. The mentioned approaches do not study the color representation of different cloud types in different color-spaces
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