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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).

Ultra-wide-field of view (UWFOV) imaging systems are affected by various aberrations, most of which are highly angle-dependent. A description of UWFOV imaging systems, such as microscopy optics, security camera systems and other special space-variant imaging systems, is a difficult task that can be achieved by estimating the Point Spread Function (PSF) of the system. This paper proposes a novel method for modeling the space-variant PSF of an imaging system using the Zernike polynomials wavefront description. The PSF estimation algorithm is based on obtaining field-dependent expansion coefficients of the Zernike polynomials by fitting real image data of the analyzed imaging system using an iterative approach in an initial estimate of the fitting parameters to ensure convergence robustness. The method is promising as an alternative to the standard approach based on Shack–Hartmann interferometry, since the estimate of the aberration coefficients is processed directly in the image plane. This approach is tested on simulated and laboratory-acquired image data that generally show good agreement. The resulting data are compared with the results of other modeling methods. The proposed PSF estimation method provides around 5% accuracy of the optical system model.

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.