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Open Access.--This article belongs to the Special Issue Remote Sensing of Clouds, 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. © 2020 by the authors., This 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).

This paper deals with evaluation and processing of astronomical image data, which are obtained by a wide-field all-sky image analyzing monitoring system (WILLIAM). The WILLIAM is an additional experimental camera for project MAIA equipped with wide field lens. The system can detect stellar objects as faint as 6th magnitude. Acquired image data are processed by an algorithm for stellar object detection and identification which is based on coordinates transfer function. Cartesian coordinates at the image data are transformed to horizontal coordinate system. This coordinate system allows searching in astronomical catalogues of stellar objects. This paper presents the components of WILLIAM, its measured electro-optical characteristics and some results of identification.

Additional monitoring equipment is commonly used in astronomical imaging. This electro-optical system usually complements the main telescope during acquisition of astronomical phenomena or supports its operation e.g. evaluating the weather conditions. Typically it is a wide-field imaging system, which consists of a digital camera equipped with fish-eye lens. The wide-field imaging system cannot be considered as a space-invariant because of space-variant nature of its input lens. In our previous research efforts we have focused on measurement and analysis of images obtained from the subsidiary all-sky monitor WILLIAM (WIde-field aLL-sky Images Analyzing Monitoring system). Space-variant part of this imaging system consists of input lens with 180 fi angle of view in horizontal and 154 fi in vertical direction. For a precise astronomical measurement over the entire field of view, it is very important to know how the optical aberrations affect characteristics of the imaging system, especially its PSF (Point Spread Function). Two methods were used for characterization of the space-variant PSF, i.e. measurement in the optical laboratory and estimation using acquired images and Zernike polynomials. Analysis of results obtained using these two methods is presented in the paper. Accuracy of astronomical measurements is also discussed while considering the space-variant PSF of the system.

Most of the classical approaches to the measurement and modeling of electro-optical imaging systems rely on the principles of linearity and space invariance (LSI). In our previous research efforts we have focused on measurement and analysis of images obtained from a double station video observation system MAIA (Meteor Automatic Imager and Analyzer). The video acquisition module of this system contains wide-field input lens which contributes to space-variability of the imaging system. For a precise astronomical measurement over the entire field of view, it is very important to comprehend how the characteristics of the imaging system can affect astrometric and photometric outputs. This paper presents an analysis of how the space-variance of the imaging system can affect precision of astrometric and photometric results. This analysis is based on image data acquired in laboratory experiments and astronomical observations with the wide-field system. Methods for efficient calibration of this system to obtain precise astrometric and photometric measurements are also proposed.

This paper presents a study of possible utilization of digital single-lens reflex (DSLR) cameras in astronomy. The DSLRs have a great advantage over the professional equipments in better cost efficiency with comparable usability for selected purposes. The quality of electro-optical system in the DSLR camera determines the area where it can be used with acceptable precision. At first a set of important camera parameters for astronomical utilization is introduced in the paper. Color filter array (CFA) structure, demosaicing algorithm, image sensor spectral properties, noise and transfer characteristics are the parameters that belong among the very important ones and these are further analyzed in the paper. Compression of astronomical images using the KLT approach is also described below. The potential impact of these parameters on position and photometric measurement is presented based on the analysis and measurements with the wide-angle lens. The prospective utilization of consumer DSLR camera as a substitute for expensive devices is discussed.

Efficient development of image processing techniques requires a database of suitable test images for verification of the performance, optimization and other related purposes. In this paper, the DEIMOS, an open-source database, is described including its structure and interface. There is a selected application example on high dynamic range content to illustrate the database features. This HDR image database contains a variety of natural scenes captured with a digital single-lens reflex camera (DSLR) under different conditions. The important capture parameters as well as the important characteristics of the camera are part of the database to ensure that the creation of each image is well documented. The DEIMOS database is created gradually step-by-step based upon the contributions of team members.

Several algorithms are used nowadays for detecting stellar objects in astronomical images, for example in the DAOPHOTprogram package and in SExtractor (Software for source extraction). Our team has become acquainted with the wavelet transform and its good localization properties. After studying the manual for DAOPHOT and SExtractor, and becoming familiar with the trous algorithm used for calculating the wavelet transform, we set ourselves the task to implement an algorithm for star detection on the basis of the wavelet transform. We focused on detecting stellar objects in complex fields, such as globular clusters and galaxies. This paper describes a stellar object detection algorithm with the help ofthe wavelet transform, and presents our results.