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Computer Optics, 2019, Volume 43, Issue 2, Pages 282–295
DOI: https://doi.org/10.18287/2412-6179-2019-43-2-282-295
(Mi co647)
 

This article is cited in 26 scientific papers (total in 26 papers)

IMAGE PROCESSING, PATTERN RECOGNITION

Complex analysis and monitoring of the environment based on earth sensing data

L. I. Lebedev, Yu. V. Yasakov, T. Sh. Utesheva, V. P. Gromov, A. V. Borusyak, V. E. Turlapov

N.I. Lobachevsky National Research State University, Gagarin Avenue 23, Russia, N.Novgorod
References:
Abstract: We study a problem of complex analysis and monitoring of the environment based on Earth Sensing Data, with the emphasis on the use of hyperspectral images (HSI), and propose a solution based on developing algorithmic procedures for HSI processing and storage. HSI is considered as a two-dimensional field of pixel signatures. Methods are proposed for evaluating the similarity of a HSI pixel signature with a reference signature, via simple alignment transformations: identical; amplitude scaling; shift along y-axis; and a combination of the last two. A clustering / recognition method with self-learning is proposed, which determines values of the transformation parameters that ensure the alignment of the current pixel signature with the reference signature. Similarity with the reference is determined by a standard deviation value. A HSI compression method with controlled losses has been proposed. The method forms a basis via accumulating reference signatures and represents the rest of the signatures by parameters matching them with the already detected class-reference signature. In an experiment with the GSI f100520t01p00-12 data of the AVIRIS spectrometer, the method provided a 2 % loss and compression coefficients of the original HSI ranging from 43 to 165 for various types of alignment transformation, while not requiring archiving and thus maintaining active access to the HSI and using the list of references as an analogue of the HSI palette. An algorithm for the formation of dense groups of detectable objects (for example, oil spots) and their nonconvex contouring, controlled by 4 parameters, is proposed.
A pilot version of the concept of geographic information system (GIS) and an appropriate database management system (DBMS) was built and implemented, which provides monitoring and is based on the prioritized processing and storage of the HSI, which serve as a data source for the system. A laboratory complex with new algorithms for processing and storing the GSE is introduced into the structure of the system.
Keywords: hyperspectral images, image processing, self-learning recognition, lossy compression, compression without archiving, non-convex contouring, digital maps, DBMS, environmental monitoring.
Funding agency Grant number
Russian Science Foundation 16-11-00068
This work was supported by the Russian Science Foundation (Project No. 16-11-00068).
Received: 06.12.2018
Accepted: 05.03.2019
Document Type: Article
Language: Russian
Citation: L. I. Lebedev, Yu. V. Yasakov, T. Sh. Utesheva, V. P. Gromov, A. V. Borusyak, V. E. Turlapov, “Complex analysis and monitoring of the environment based on earth sensing data”, Computer Optics, 43:2 (2019), 282–295
Citation in format AMSBIB
\Bibitem{LebYasUte19}
\by L.~I.~Lebedev, Yu.~V.~Yasakov, T.~Sh.~Utesheva, V.~P.~Gromov, A.~V.~Borusyak, V.~E.~Turlapov
\paper Complex analysis and monitoring of the environment based on earth sensing data
\jour Computer Optics
\yr 2019
\vol 43
\issue 2
\pages 282--295
\mathnet{http://mi.mathnet.ru/co647}
\crossref{https://doi.org/10.18287/2412-6179-2019-43-2-282-295}
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  • https://www.mathnet.ru/eng/co647
  • https://www.mathnet.ru/eng/co/v43/i2/p282
  • This publication is cited in the following 26 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    Computer Optics
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    References:28
     
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