Hyperspectral regression lossless compression algorithm of aerospace images

Sarinova, Assiya; Zamyatin, Alexander

doi:10.1051/e3sconf/202014902003

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…By modify the basis of the matrix (H , H 8 ) for each submatrix, conducting both forward and inverse computations. During this process, we calculate each element of the transformed submatrix using the following formulas: (17) (18) where n -size of the Hadamard matrix, I [m, n, k] -submatrix of the original matrix, H w -Hadamard matrix, i -row of current value in submatrix, j -current value column in submatrix, H -basis of the matrix.…”

Section: The Results Will Be Included In the Matrixmentioning

confidence: 99%

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Mathematical Framework Formulation and Implementation for Hyperspectral Aerospace Images Processing

Sarinova,

Neftissov,

Rzayeva

et al. 2023

sjaitu

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This paper proposes a preprocessing algorithm for aerospace hyperspectral images based on a mathematical apparatus effectively applied in pre-compression transformation problems. In particular, several methods have been analyzed for hyperspectral image (signal) preprocessing from the point of view of digital signal processing algorithms. These mathematical methods are used for problems of filtering signals from noise of different natures and for compression and restoration of signals after their transmission through communication channels. The results of comparative analysis of preparatory processing of lossy compression algorithms based on wavelet analysis, discrete and orthogonal transforms are also given, demonstrating minimization of loss level of reconstructed decoded images. The performance of the proposed preprocessing algorithms with quality metrics is presented to evaluate the quality of the reconstructed hyperspectral aerospace images. The results of this study can be applied and used in the tasks of special processing of hyperspectral images, as well as fundamental knowledge of mathematical apparatuses of the proposed orthogonal preprocessing, considering the specificity of the data which is very important in obtaining images ready for compression for the subsequent identification of objects of the Earth's surface and using such mathematical transformations at the hyperspectral image preprocessing stage before compression provides efficient archiving of the obtained data, while reducing the communication channel load. Through the use of quality metrics of the reconstructed images, the preprocessing algorithm provides an understanding of the threshold of the peak signal-to-noise ratio value and the efficiency of its application to calculate and minimize the loss rate.

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Section: The Results Will Be Included In the Matrixmentioning

confidence: 99%

“…3. Transforming a data structure based on the original HAI storing coefficient values, using the discrete cosine transform as an example [16][17][18].…”

Section: Pre-processing Algorithm For Aerospace Imagesmentioning

confidence: 99%

Mathematical Framework Formulation and Implementation for Hyperspectral Aerospace Images Processing

Sarinova,

Neftissov,

Rzayeva

et al. 2023

sjaitu

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show abstract

“…Currently, the development of software systems for lossy data compression is an urgent task. In solving this problem, there are various areas of research in which research is actively being conducted in the field of developing compression algorithms [1][2][3][4][5][6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations

Sarinova

2021

E3S Web Conf.

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The paper describes the development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations for the purpose of subsequent compression in Earth remote sensing systems. As compression algorithms necessary to reduce the amount of transmitted information, it is proposed to use the developed compression methods based on Walsh-Hadamard transformations and discrete-cosine transformation. The paper considers a methodology for developing lossy and high-quality compression algorithms during recovery, taking into account which an adaptive algorithm for compressing hyperspectral AI and the generated quantization table has been developed. The conducted studies have shown that the proposed lossy algorithms have sufficient efficiency for use and can be applied when transmitting hyperspectral remote sensing data in conditions of limited buffer memory capacity and bandwidth of the communication channel.

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Methodology for Developing Algorithms for Compressing Hyperspectral Aerospace Images used on Board Spacecraft

Sarinova

Zamyatin

2020

E3S Web Conf.

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The paper describes a method for constructing and developing algorithms for compressing hyperspectral aerospace images (AI) of hardware implementation for subsequent use in remote sensing Systems (RSS). The developed compression methods based on differential and discrete transformations are proposed as compression algorithms necessary for reducing the amount of transmitted information. The paper considers a method for developing compression algorithms, which is used to develop an adaptive algorithm for compressing hyperspectral AI using programmable devices. Studies have shown that the proposed algorithms have sufficient efficiency for use and can be applied on Board spacecraft when transmitting hyperspectral remote sensing data in conditions of limited buffer memory capacity and communication channel bandwidth.

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Hyperspectral regression lossless compression algorithm of aerospace images

Cited by 6 publications

References 11 publications

Mathematical Framework Formulation and Implementation for Hyperspectral Aerospace Images Processing

Mathematical Framework Formulation and Implementation for Hyperspectral Aerospace Images Processing

Development of compression algorithms for hyperspectral aerospace images based on discrete orthogonal transformations

Methodology for Developing Algorithms for Compressing Hyperspectral Aerospace Images used on Board Spacecraft

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