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Informatics and Automation, 2021, Issue 20, volume 2, Pages 302–340
DOI: https://doi.org/10.15622/ia.2021.20.2.3
(Mi trspy1145)
 

This article is cited in 5 scientific papers (total in 5 papers)

Digital Information Telecommunication Technologies

Formation of a fused image of the land surface based on pixel clustering of location images in a multi-position onboard system

V. Nenasheva, I. Khanykovb

a Saint-Petersburg State University of Aerospace Instrumentation
b St. Petersburg Federal Research Center of the Russian Academy of Sciences
Abstract: The paper proposes a method for fusioning multi-angle images implementing the algorithm for quasi-optimal clustering of pixels to the original images of the land surface. The original multi-angle images formed by the onboard equipment of multi-positional location systems are docked into a single composite image and, using a high-speed algorithm for quasi-optimal pixel clustering, are reduced to several colors while maintaining characteristic boundaries. A feature of the algorithm of quasi-optimal pixel clustering is the generation of a series of partitions with gradually increasing detail due to a variable number of clusters. This feature allows you to choose an appropriate partition of a pair of docked images from the generated series.
The search for reference points of the isolated contours is performed on a pair of images from the selected partition of the docked image. A functional transformation is determined for these points. And after it has been applied to the original images, the degree of correlation of the fused image is estimated. Both the position of the reference points of the contour and the desired functional transformation itself are refined until the evaluation of the fusion quality is acceptable. The type of functional transformation is selected according to the images reduced in color, which later is applied to the original images. This process is repeated for clustered images with greater detail in the event that the assessment of the fusion quality is not acceptable. The purpose of present study is to develop a method that allows synthesizing fused image of the land surface from heteromorphic and heterogeneous images.
The paper presents the following features of the fusing method. The first feature is the processing of a single composite image from a pair of docked source images by the pixel clustering algorithm, what makes it possible to isolate the same areas in its different parts in a similar way. The second feature consists in determining the functional transformation by the isolated reference points of the contour on the processed pair of clustered images, which is later applied to the original images to combine them.
The paper presents the results on the synthesis of a fused image both from homogeneous (optical) images and from heterogeneous (radar and optical) images. A distinctive feature of the developed method is to improve the quality of synthesis, increase the accuracy and information content of the final fused image of the land surface.
Keywords: pixel clustering, multi-angle images, synthesis of fused images, location onboard systems, full-dimension images of the land surface, reference points on contour, information integration, unmanned aerial vehicle, multi-position system.
Funding agency Grant number
Russian Science Foundation 19-79-00303
The reported study was funded by a grant of Russian Science Foundation (project № 19-79-00303).
Received: 16.12.2020
Bibliographic databases:
Document Type: Article
UDC: 004.932
Language: Russian
Citation: V. Nenashev, I. Khanykov, “Formation of a fused image of the land surface based on pixel clustering of location images in a multi-position onboard system”, Informatics and Automation, 20:2 (2021), 302–340
Citation in format AMSBIB
\Bibitem{NenKha21}
\by V.~Nenashev, I.~Khanykov
\paper Formation of a fused image of the land surface based on pixel clustering of location images in a multi-position onboard system
\jour Informatics and Automation
\yr 2021
\vol 20
\issue 2
\pages 302--340
\mathnet{http://mi.mathnet.ru/trspy1145}
\crossref{https://doi.org/10.15622/ia.2021.20.2.3}
\elib{https://elibrary.ru/item.asp?id=45594819}
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  • https://www.mathnet.ru/eng/trspy/v20/i2/p302
  • This publication is cited in the following 5 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    Informatics and Automation
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