thematic processing
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The article describes the problem of automated recognition of the anthropogenic elements of landscape. The recognition is based on the aerospace data in the optical range of the spectrum and a feature model of an object, consisting of the geometric and reflectance characteristics. Using this model, we formed training samples for a convolutional neural network. There is a real example of the practical implementation of the model in identification of the aviation objects.

The need of the creation of information system with specialized services which allow scientists and specialists to perform thematic processing of Earth remote sensing data, changing the data processing parameters in a certain way, and independently analyze the information received is shown.To achieve the goal of rapid provision of information, the development of specialized information system is considered on the example of the creation of the software package for the dissemination of operational space information, as well as a software package for predicting the yield of grain crops.The structure of the software package for the dissemination of operational space information is shown. The technical and functional characteristics of the separate components of the package are given. The relations of the subsystems of the package are shown during data receiving, processing, analyzing and providing information to interested specialists and researchers. Attention is drawn to the possibilities of wide application of the software package for the dissemination of operational space information for solving scientific and applied problems in various fields of knowledge.The practical application of vegetation indexes in the analysis of operational space information is shown. The data of the practical results of forecasting of the grain crops development, as well as the quantitative indicators of satellites sessions are presented. The possibility of further improvement and development of the created information system is shown.

We describe an algorithm based on a convolutional neural network that detects cloud and snow covers in satellite images. Algorithm accuracy was evaluated using machine learning metrics. The proposed algorithm is fully automatic

Statistical texture features are frequently used for the thematic processing of very high spatial resolution satellite images. The assessment of information content of 1st and 2nd order statistics is carried out based on processing WorldView-2 images of test areas located on the territory of the Savvatyevskoe forestry and employing the corresponding ground-based data. The comparison of the accuracy and computational efficiency of traditional and ensemble classifiers in the problem of pattern recognition of various natural and man-made objects reveals the high performance of the error correcting output codes method. The estimates obtained in this study demonstrate the advantage of using ensemble classification and 2nd order statistical texture features.

The article describes the capability of application of RGB?1 images made by digital cameras for detection of Earth surface (vegetation) types. A set of measures necessary to be taken for processing of the images made by unmanned aerial vehicles (UAV) in real-time is described. Application of analogue of vegetation index allows detecting vegetation on an RGB image, which increases the probability of correct detection of surface (vegetation) type. Methods of preliminary and thematic processing of images required for positive detection of surface types are considered. Texture analysis is applied for detection of vegetation type. The results of the processing of real images are provided.

The possibility of detecting debris fragments of separating parts of launch vehicles on the ground on the basis of video spectral (hyperspectral) aerial survey data processing in the range of 0.4-1.0 µm is considered. Evaluation of the quality of special methods pre-correction when comparing, during thematic processing, measures the spectral vicinity – the special delta-vector metric, modified metric Terebizh, factor correlations, and sub-pixel method.

The article is devoted to the use of remote sensing data for assessing the anthropogenic impact on ecosystems on the example of arid areas. It describes main advantages and problems of the data processing methods, including automated interpretation of images. The technique of thematic processing of a time series of space images is proposed.