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Application of a doubly stochastic autoregressive model for solving problems in satellite images processing


V.E. Dement'ev – Ph. D. (Eng.), Associate Professor, Department «Telecommunications»,
Ulyanovsk State Technical University

Satellite systems for recording areas of the earth's surface have been widely used recently. This is due to the wide possibilities that remote sensing of the Earth (RS) provides. Indeed, satellite data has a number of significant advantages over conventional cartographic information. The key ones are the accuracy and efficiency of remote sensing data, as well as the possibility of monitoring in different spectral ranges. Therefore, space images are the best means of operational control and monitoring tasks. Unlike maps, they allow you to quickly identify new phenomena and their development processes.
One of the key features of satellite imagery is their pronounced spatial heterogeneity. It is associated with the variety of shapes and textures of different objects observed from space. Indeed, any satellite image of the Earth's surface contains images of different objects, for example, rivers, forests, urban buildings, agricultural lands, etc. The visual characteristics of these objects are significantly different. An attempt to describe the whole picture using known homogeneous models (for example, autoregressive, wave, etc.) leads to errors caused by incorrect averaging of the information. In this regard, to describe satellite images, it is necessary to apply more complex mathematical models capable of simulating inhomogeneous random fields (SP). In the present work, a study is made of doubly stochastic autoregressive SP models that can serve as an adequate mechanism for describing satellite heterogeneous images.

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June 24, 2020
May 29, 2020

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