Mr. Dmitry V. Uchaev Profile

Dmitry V. Uchaev

at Moscow State Univ of Geodesy and Cartography

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Author

Publications (9)

Proceedings Article | 13 September 2021 Presentation + Paper

Multifractal classification of Sentinel-1 SAR images of ice-covered sea areas

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

Proceedings Volume 11862, 118620S (2021) https://doi.org/10.1117/12.2599886

KEYWORDS: Synthetic aperture radar, Image classification, Remote sensing, Fractal analysis

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Proceedings Article | 12 September 2021 Poster + Presentation + Paper

Feature profiles for semisupervised hyperspectral image classification with limited labeled training samples

D. V. Uchaev, Dm. V. Uchaev

Proceedings Volume 11862, 118620W (2021) https://doi.org/10.1117/12.2599182

KEYWORDS: Image classification, Hyperspectral imaging, Fractal analysis, Feature extraction, Machine learning

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Proceedings Article | 14 October 2020 Presentation + Paper

Spectral-spatial classification of hyperspectral images based on multifractal features

Dmitry Uchaev, Denis Uchaev, Vasily Malinnikov

Proceedings Volume 11533, 115330T (2020) https://doi.org/10.1117/12.2573715

KEYWORDS: Hyperspectral imaging, Image classification, Feature extraction, Image analysis, Image scaling, Basic research

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Classification of hyperspectral images is an important step of hyperspectral image interpretation. Different studies demonstrate that spatial features can provide complementary information for increasing the accuracy of hyperspectral image classification. In this study, we propose a method of spectral-spatial classification of hyperspectral images that is based on the use of specific multifractal features as the spatial features. The proposed method of hyperspectral image classification consists of the following steps. First, informative multifractal features are extracted from first few principal components of spectral features. For construction of the multifractal features, in the windows centered on each element of principal component images, using a generalized local-global multifractal image analysis, various 1D and 2D multiracial characteristics can be calculated including our early introduced 2D multifractal characteristics of global scaling exponents. After that, obtained multifractal features are stacked with spectral features into high-dimensional feature vectors. Finally, the resulting high-dimensional vectors of spectral and multifractal features are classified by a support vector machine classifier. The multifractal characteristics that are used to construct multifractal features have a lot of advantages: these characteristics provide a good textural separability of image objects, demonstrate an invariance to image scaling and rotation, and they are also insensitive to image noise. The experiments performed on several widely known test hyperspectral images have demonstrated that proposed method exhibits better performance than competitive methods of spectral-spatial classification of hyperspectral images, in terms of the overall accuracy and kappa statistic. In addition, it is shown that the introduced classification method can outperform some deep learning methods of hyperspectral image classification, which in recent years have attracted great interest in hyperspectral image classification. In particular, it was established that the proposed method can achieve good classification results over deep learning methods if we use small training samples for classification. In the future, we will focus on developing methods for object-oriented classification of hyperspectral images, which are based on the use of multifractal features. The study has been supported by the Ministry of Education and Science of the Russian Federation (Project No. МК-3477.2019.5) and by the Russian Foundation for Basic Research (Project No. 19-05-00330 А)."

Proceedings Article | 31 January 2020 Paper

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

Dm. Uchaev, D. Uchaev, V. Malinnikov

Proceedings Volume 11433, 1143307 (2020) https://doi.org/10.1117/12.2559391

KEYWORDS: Synthetic aperture radar, Image analysis, Bandpass filters, Image scaling, Image classification, Fractal analysis, Image processing

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Proceedings Article | 31 January 2020 Paper

Theory and methodology of multifractal interpretation of aerospace images

Dm. Uchaev, D. Uchaev

Proceedings Volume 11433, 114333B (2020) https://doi.org/10.1117/12.2559168

KEYWORDS: Aerospace engineering, Image segmentation, Systems modeling, Remote sensing, Fractal analysis, Image classification, Correlation function, Statistical analysis, Image processing

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SPIE Journal Paper | 6 January 2017

Chebyshev-based technique for automated restoration of digital copies of faded photographic prints

Dmitry Uchaev, Denis Uchaev, Vasily Malinnikov

JEI, Vol. 26, Issue 01, 011024, (January 2017) https://doi.org/10.1117/12.10.1117/1.JEI.26.1.011024

KEYWORDS: Image enhancement, Photography, Image contrast enhancement, Autoregressive models, Curium, Image quality, Distortion, Image processing, Algorithm development, Wavelets

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Proceedings Article | 8 December 2015 Paper

Image contrast enhancement using Chebyshev wavelet moments

Dm. Uchaev, D. Uchaev, V. Malinnikov

Proceedings Volume 9875, 987512 (2015) https://doi.org/10.1117/12.2228603

KEYWORDS: Image enhancement, Image contrast enhancement, Wavelets, Curium, Selenium, Image segmentation, Image analysis, Image quality, Autoregressive models, Visualization

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Proceedings Article | 14 February 2015 Paper

Orthogonal wavelet moments and their multifractal invariants

Dm. Uchaev, D. Uchaev, V. Malinnikov

Proceedings Volume 9445, 94450U (2015) https://doi.org/10.1117/12.2180706

KEYWORDS: Wavelets, Image restoration, Curium, Image compression, Image analysis, Denoising, Pattern recognition, Digital imaging, Multiscale representation, Error analysis

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Proceedings Article | 14 February 2015 Paper

Orthogonal wavelet moments and their multifractal invariants

Dm. Uchaev, D. Uchaev, V. Malinnikov

Proceedings Volume 9445, 94450U (2015) https://doi.org/10.1117/12.2180706

KEYWORDS: Wavelets, Image restoration, Curium, Image compression, Image analysis, Denoising, Pattern recognition, Digital imaging, Multiscale representation, Error analysis

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Showing 5 of 9 publications

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