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RAS Chemistry & Material ScienceХимическая физика Advances in Chemical Physics

  • ISSN (Print) 0207-401X
  • ISSN (Online) 3034-6126

METHOD FOR EVALUATING THE EFFICIENCY OF HYPERSPECTRAL IMAGING INSTRUMENTATION FOR DETECTING GAS CLOUDS AND PLUMES

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PII
S3034612625120102-1
DOI
10.7868/S3034612625120102
Publication type
Article
Status
Published
Authors
I. D. Rodionov
  • Affiliation: Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences
A. N. Vinogradov
  • Affiliation: AO Scientific and Technical Center Reagent
M. A. Gomorev
  • Affiliation: AO Scientific and Technical Center Reagent
Y. A. Izmailova
  • Affiliation: AO Scientific and Technical Center Reagent
A. I. Rodionov
  • Affiliation: Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences
I. P. Rodionova
  • Affiliation: Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences
G. A. Shvetsov
  • Affiliation: AO Scientific and Technical Center Reagent
Y. A. Dyakov
  • Affiliation: Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences
D. V. Shestakov
  • Affiliation: Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences
M. G. Golubkov
  • Affiliation: Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences
Volume/ Edition
Volume 44 / Issue number 12
Pages
90-108
Abstract
The releases of toxic or explosive gases have a negative impact on the environment and pose a serious threat to the life and health of industrial workers, as well as to the population living in areas adjacent to the industrial facilities. Modern technologies make it possible to remotely and promptly detect such threats, thereby preventing potential accidents and disasters. This work presents a novel methodology for simulating the detection of a gas cloud resulting from a leak at an industrial infrastructure line under open atmospheric conditions. The approach includes the synthesis of observation scenarios in the radiation wavelength range of 300–2500 nm, taking into account the peculiarities of its detection utilizing hyperspectral imaging instrumentation (HSI). Using the example of sulfur dioxide leak detection via a neural network algorithm based on a Siamese neural network, it has been demonstrated that an SO cloud can be remotely identified using HSI operating in the 330–700 nm range with a spectral resolution of 1 nm.
Keywords
гиперспектральная аппаратура спектральное разрешение дистанционное обнаружение утечек газов моделирование методика оценки эффективности сечение молекулярного поглощения гауссова модель рассеивания модель Паскуилла—Гиффорда синтезирование данных нейросетевые алгоритмы сиамская нейронная сеть
Date of publication
03.03.2026
Year of publication
2026
Number of purchasers
0
Views
54

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