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Representation and Processing of Spatially Heterogeneous Images and Image Sequences

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Computer Vision in Advanced Control Systems-5

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 175))

Abstract

In the present chapter, the problems of multidimensional images and image sequences representation and processing within the framework of the Earth remote sensing problems solving are considered. The issues of correlated data multidimensional arrays description and optimal and suboptimal processing thereof based on formalized mathematical models are investigated. It is proposed to use doubly stochastic autoregressive models as the basis for such models. It is shown that similar models enable to describe two essential properties of the actual satellite material. Firstly, the construction of doubly stochastic models allows for description of multidimensional random fields and time sequences thereof. This is of fundamental importance for real-time sequences of multispectral images each being considered as brightness values 3D array composed of separate two-dimensional frames, which correspond to the Earth surface registration results in a separate spectral band. A time sequence of such images is equivalent to four-dimensional random field with one-dimension corresponding to discrete time. Secondly, doubly stochastic models enable to carry out estimation of spatially heterogeneous images, i.e. images having probabilistic properties, which vary with spatial coordinates. Such variations are typical for actual satellite snapshots containing objects of various nature: rivers, forests, fields, etc. The chapter contains investigation of probabilistic and correlation properties of doubly stochastic models. Algorithms for these models parameters estimation based on actual satellite signals observations are proposed. It is shown that basing on the proposed models it is possible to synthesize multidimensional images filtering algorithms allowing for spatial heterogeneity of the images. Several classes of such algorithms enabling also to carry out processing of real-time sequences of multispectral satellite images are proposed. It has been established that the found algorithms possess higher effectiveness in comparison with known analogues. In this study, the problem of extended objects detection against the background of multispectral images time sequences is considered. A family of detection algorithms based on preliminary nonlinear filtering of these images is synthesized. Application of the developed algorithms, when solving problems of satellite images classification and natural or man-made objects monitoring, is briefly considered.

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Acknowledgements

The reported study was funded by the Russian Fund for Basic Researches according to the research projects â„– 16-41-732041 and â„– 18-47-730009.

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Authors and Affiliations

  1. Ulyanovsk State Technical University, 32 Severny Venets St., Ulyanovsk, 432027, Russian Federation

    Vitaly E. Dement’ev, Victor R. Krasheninnikov & Konstantin K. Vasil’ev

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  1. Vitaly E. Dement’ev
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  2. Victor R. Krasheninnikov
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  3. Konstantin K. Vasil’ev
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Correspondence to Victor R. Krasheninnikov .

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Editors and Affiliations

  1. Department of Informatics and Computer Techniques, Institute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russia

    Margarita N. Favorskaya

  2. University of Canberra, Canberra, SA, Australia

    Lakhmi C. Jain

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Dement’ev, V.E., Krasheninnikov, V.R., Vasil’ev, K.K. (2020). Representation and Processing of Spatially Heterogeneous Images and Image Sequences. In: Favorskaya, M., Jain, L. (eds) Computer Vision in Advanced Control Systems-5. Intelligent Systems Reference Library, vol 175. Springer, Cham. https://doi.org/10.1007/978-3-030-33795-7_3

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