Chebyshev multifractal signatures and their use in multifractal interpretation of SAR images of ice-covered sea areas

Dm. V. Uchaev; D. V. Uchaev; V. A. Malinnikov

doi:10.1117/12.2559391

31 January 2020 Chebyshev multifractal signatures and their use in multifractal interpretation of SAR images of ice-covered sea areas

Dm. V. Uchaev, D. V. Uchaev, V. A. Malinnikov

Author Affiliations +

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 1143307 (2020) https://doi.org/10.1117/12.2559391
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

Chebyshev multifractal signatures for characterization of a multifractal nature of image textures for natural objects are proposed. These signatures can be obtained by generalized multifractal formalism (GMF) using Chebyshev polynomial (CP) kernels and can be considered as alternatives to traditional multifractal spectra. The paper also presents properties of introduced multifractal signatures: in particular, it is shown that Chebyshev multifractal signatures, similar to traditional multifractal spectra, are invariant to image scaling. To illustrate recognition possibilities of multifractal signatures, the application of the signatures for multifractal interpretation of synthetic-aperture radar (SAR) images of ice-covered sea areas are shown. It is established that using parameters of multifractal signature approximations calculated by Sentinel-1 SAR image regions, we can separate sea areas with very close ice, close ice and very open ice. The obtained results allow to say that it is possible to use the introduced multifractal signatures at the preliminary stage of object-oriented classification of SAR or other images to assess a textural separability of image objects.

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Dm. V. Uchaev, D. V. Uchaev, and V. A. Malinnikov "Chebyshev multifractal signatures and their use in multifractal interpretation of SAR images of ice-covered sea areas", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 1143307 (31 January 2020); https://doi.org/10.1117/12.2559391

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