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An approach to the retrieval of sea wave spatial spectra based on satellite optical imagery in linear and nonlinear approximations is described. Physical mechanisms of the formation of disturbed sea surface brightness fields recorded by remote sensing equipment are analyzed. Wave spectra retrieval methods using brightness field formation models that consider linear and nonlinear dependencies on sea surface slopes are suggested. A method for the construction of operators that retrieve the spatial spectra of surface wave slopes and elevations from aerospace imagery and take into account nonlinear modulations of disturbed sea surface brightness fields is developed. This method is based on the numerical simulation of sea surface images and the construction of a retrieving operator with respect to a set of parameters determined by aerospace imaging conditions. Examples of the use of the developed methods are given.

The results of studying sea-wave spectra in a wide wavelength range using high resolution (0.5–1.0 m) satellite optical imagery spectra and the results of measurements carried out from an oceanographic platform using string-wave recorders, stereo system, and drifting wave buoys are presented in this paper. The wave spectra retrieved from satellite imagery and sea-truth data have been compared. A comparison has shown the adequacy of the purposely developed retrieval methods. Power approximation indices for spatial spectra in the 0.04–5.0-m wavelength range have been found. It has been shown that the wave spectra measured experimentally by satellite-based and in-situ methods best approximate the Toba spectrum.

A method to register sea-wave spectra using optical aerospace imagery has been developed. The method is based on the use of retrieval operators both in areas of high and low spatial frequencies, including the areas of spectral maximum. The approach to adjust and validate the method developed using sea truth data obtained by string wave recorders has been suggested. This paper presents the results of using the suggested method to study sea-wave spectra using high-resolution satellite imagery for various water areas under different conditions of wave generation.

This paper presents a method for preparing data for machine learning for semantic segmentation of informative classes in images based on clustering for solving problems of space monitoring of impact areas. A classification of clustering methods by various criteria is given. The choice of hierarchical clustering methods as the most effective for working with clusters of arbitrary structure and shape is substantiated. A general scheme for calculating a clustering model is given, which includes, in addition to the clustering itself, procedures for data tiling, estimating the optimal clustering parameters, registering objects, and assessing the quality of the obtained data. A scheme for preparing data for machine learning is shown, including the construction of a reference markup, calculation of a clustering model, markup correction, and testing the obtained clustering models for different informative classes on new images.

An Erratum to this paper has been published: https://doi.org/10.1134/S1028334X23050136

The development of a method of retrieval of two-dimensional spatial spectra of sea wave elevations is proposed on the basis of high-resolution satellite imagery, which permits estimation of the angular distributions of wind wave energy. The method is validated by the results of a comprehensive experiment that involved satellite imaging of the Black Sea water area using optical instruments and sea truth measurements under controlled conditions from a stationary oceanographic platform. The angular distribution of sea wave energy retrieved by spatial spectra of satellite imagery fragments was compared with the results of measurements of the frequency-angular spectra collected using an array of string wave recorders. It is shown that the results of remote and in situ measurements are consistent in the range of sea wavelengths from 2.8 to 30 m and that the average absolute error is 0.3.

A method for the three-dimensional reconstruction of buildings from a single aerospace image, which consists of two stages—the extraction of semantic information and the restoration of the geometry—is described. The topology of artificial neural networks by the semantic segmentation of building components and reference objects is considered. In the second stage, some mathematical transformations are presented: by calculating the photometric parameters of an image based on metadata or reference objects, by converting spatial coordinates into axial and flat image coordinates, etc. Two examples are shown for calculating photometric parameters and a three-dimensional building model from a single satellite image and an aerial photograph.

Development of biometric human identification algorithms and systems is reviewed. A crisis that prevents them from being used in large computing systems is demonstrated. Comprehensive reliable tests are considered as a way to overcome the crisis. Ways to take into account the influence various factors may have on the reliability of evaluating the practical efficiency of remote human identification algorithms and systems are addressed. A method to determine and calculate quantitative quality criteria of multimodal biometric data is proposed; the possibility of extrapolating the results of testing biometric algorithms to practical application is discussed. Quality functions and the artificiality of biometric data given as the measure of proximity to biometric data registered from an unaware recognition subject are studied.

A primary consideration of this paper is to determine different factors influencing the reliability of performance evaluations of remote person recognition algorithms and systems. The authors suggest a method for determining and computing quantitative quality criteria of multimodal biometric data and consider the possibility of extrapolating test results to various practical applications. The functions of biometric data quality and biometric data artificiality that are introduced as a measure of proximity of the available biometric data to biometric data registered “naturally,” i.e., data of unaware and noncollaborative subjects, are under examination in this paper.

A probabilistic method for improving the spatial resolution of multispectral space images using a reference image is proposed. The developed method calculates the mathematical expectation of pixel brightness in different channels of an improved multispectral image based on the probabilistic characteristics of the pixel neighborhood on the multispectral image and the overall brightness intensity of the panchromatic image at that point. The applicability of different metrics for evaluating the quality of the spatial resolution of satellite images is analyzed. A set of the most adequate quality evaluation metrics is used. An optimization procedure is developed to adjust the parameters of the proposed probabilistic resolution improvement method. The results of testing the method on the multi-spectral images obtained from different satellites in different spatial resolutions are presented. The efficiency of the algorithm is tested at different magnification scales. A comparative analysis of the results of the proposed method with similar approaches is conducted.