Investigation of the similarity algorithm of the satellite images storage system for stability on the basis of Haar wavelets according to Tikhonov

Kazaryan, Maretta; Shahramanian, Mikhail; Zabunov, Svetoslav

doi:10.3897/arb.v31.e06

Cited by 4 publications

(6 citation statements)

References 9 publications

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“…In fact, the fractal wavelets constitute the foundation of this class. The old Haar wavelet (also known as the lazy wavelet), as a typical empirical transform, works very well with images, as recently reported in [16,17]. The more sophisticated Walsh-Hadamard Transform (still empirical, though) can also be adapted to process images-see, for example, [18,19] (where it works in combination with Haar wavelet).…”

Section: Introductionmentioning

confidence: 89%

“…Definition ( 21), together with Definition (17) and Equation (19), enable the analysis of the orthogonality of 2D tfas by means of the orthogonality of corresponding 1D tfas. All the orthogonality and invertibility results proven in [3] for the 1D time-frequency dictionaries of JvN atoms can straightforwardly be transferred to 2D space-frequency dictionaries.…”

Section: Jvnt For Discrete-space 2d Signalsmentioning

confidence: 99%

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John von Neumann’s Space-Frequency Orthogonal Transforms

Stefanoiu,

Culita

2024

Mathematics

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Among the invertible orthogonal transforms employed to perform the analysis and synthesis of 2D signals (especially images), the ones defined by means of John von Neumann’s cardinal sinus are extremely interesting. Their definitions rely on transforms similar to those employed to process time-varying 1D signals. This article deals with the extension of John von Neumann’s transforms from 1D to 2D. The approach follows the manner in which the 2D Discrete Fourier Transform was obtained and has the great advantage of preserving the orthogonality property as well as the invertibility. As an important consequence, the numerical procedures to compute the direct and inverse John von Neumann’s 2D transforms can be designed to be efficient thanks to 1D corresponding algorithms. After describing the two numerical procedures, this article focuses on the analysis of their performance after running them on some real-life images. One black and white and one colored image were selected to prove the transforms’ effectiveness. The results show that the 2D John von Neumann’s Transforms are good competitors for other orthogonal transforms in terms of compression intrinsic capacity and image recovery.

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Section: Introductionmentioning

confidence: 89%

Section: Jvnt For Discrete-space 2d Signalsmentioning

confidence: 99%

John von Neumann’s Space-Frequency Orthogonal Transforms

Stefanoiu,

Culita

2024

Mathematics

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“…Earth remote sensing technologies enable research in hard-to-reach geographical areas, such as deserts, seafloors, mountains, etc., which have recently become popular tourist destinations, thereby causing environmental damage to recreational areas [2][3][4][5][6].…”

Section: The Tourism Development Modelmentioning

confidence: 99%

Space monitoring using earth remote sensing technologies and stochastic geometry to analyze emergency situations

Kazaryan,

Shakhramanian,

Semenishchev

2024

Computational Optics 2024

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The article proposes a technology for operating an automatic space monitoring system for the presence of waste disposal sites. The technology is based on the ideas of stochastic geometry, as well as geometric probability and covariograms. The paper proposes an algorithm based on the trace transform using discrete orthogonal transforms to minimize the feature space. The problem of developing a trace matrix and selecting informative features using the stochastic geometry method for finding waste disposal sites from high-resolution satellite images is studied using the orthogonal transformations apparatus. The proposed methodology is tested using space images depicting waste disposal sites.

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“…Certain techniques have been developed for space monitoring of solid household waste (SHW) and industrial waste (IW) [1,2,3,5,6,7]. In particular:…”

Section: Introductionmentioning

confidence: 99%

“…The form of the matrix R is used in the discrete interpretation of the problem on image compression with regularization, although it was defined for the continuous case [5,13].…”

mentioning

confidence: 99%

Waste disposal facilities monitoring based on high-resolution information features of space images

et al. 2020

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In the article there is represented and solved the problem of space images recognition for the presence of solid household and industrial waste without binarization. The methods of stochastic geometry and mathematical analysis are used. In the work there is proposed an algorithm based on a trace transformation using discrete orthogonal transformations (DOT) to minimize the attribute space and carry out studies on correctness by Tikhonov. For the implementation of the algorithm there are used elements of mathematical analysis, wavelet analysis, functional analysis, theory of discrete orthogonal transformations, methods for deciphering space images in the problem of stochastic scanning of space images based on the formation of a triplet attribute with minimization of attribute space using DOT. The development a trace matrices and the selection of informative features by stochastic geometry to find WDF from high-resolution space images are investigated from the point of view of DOT apparatus application. A study of the sustainability task was also performed. The proposed technique was tested using the example of space photographs with a WDF image. Conclusions are drawn on the use of the method proposed in this article for the task of automatic computer generation and selection of informative features for determining waste disposal facilities from high-resolution space images. It is proposed to use the Tikhonov regularization method to introduce stability in this task.

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Investigation of the similarity algorithm of the satellite images storage system for stability on the basis of Haar wavelets according to Tikhonov

Abstract: Investigation of the similarity algorithm of the satellite images storage system for stability on the basis of Haar wavelets according to Tikhonov

Cited by 4 publications

References 9 publications

John von Neumann’s Space-Frequency Orthogonal Transforms

John von Neumann’s Space-Frequency Orthogonal Transforms

Space monitoring using earth remote sensing technologies and stochastic geometry to analyze emergency situations

Waste disposal facilities monitoring based on high-resolution information features of space images

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