loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Nina Aprausheva 1 ; Irina Gorlach 2 ; Aleksandr Zhelnin 1 and Stanislav Sorokin 1

Affiliations: 1 Computing Center: Russian Academy of Sciences, Russian Federation ; 2 Hydrometeorological Research Center of Russian Federation, Russian Federation

Keyword(s): Multy-spectral satelite data, approximation, Gaussian mixture, estimates of maximum likelihood, Day-Shlesingner algorithm, testing composite hypotheses, Bayes rule.

Abstract: Results of the recognition of multi-spectral satellite data by an automated classification procedure (ACP) are presented. The procedure is based on the approximation of an unknown probability density of a given set of observations by a multi-dimensional Gaussian mixture. For a given number of mixture components, optimal estimates for unknown parameters are found by the Day-Shlezinger algorithm as such solution of simultaneous likelihood equations, that maximizes the likelihood function. Optimal number of classes is determined by the step-by-step testing of two composite statistical hypotheses. The classification of a set of observations is performed by the Bayes rule. To reduce the calculus number, a preliminary analysis of the structure of the investigated set is carried out, which provides rough estimates of the number of classes and their basic characteristics. Results of automatic classification of the main types of clouds and underlying surface are described.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.238.118.80

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Aprausheva, N.; Gorlach, I.; Zhelnin, A. and Sorokin, S. (2013). On Automated Recognition of Cloud Types Instructions. In Proceedings of the 4th International Workshop on Image Mining. Theory and Applications - IMTA-4, (VISIGRAPP 2013) ISBN 978-989-8565-50-1, pages 114-120. DOI: 10.5220/0004395001140120

@conference{imta-413,
author={Nina Aprausheva. and Irina Gorlach. and Aleksandr Zhelnin. and Stanislav Sorokin.},
title={On Automated Recognition of Cloud Types Instructions},
booktitle={Proceedings of the 4th International Workshop on Image Mining. Theory and Applications - IMTA-4, (VISIGRAPP 2013)},
year={2013},
pages={114-120},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004395001140120},
isbn={978-989-8565-50-1},
}

TY - CONF

JO - Proceedings of the 4th International Workshop on Image Mining. Theory and Applications - IMTA-4, (VISIGRAPP 2013)
TI - On Automated Recognition of Cloud Types Instructions
SN - 978-989-8565-50-1
AU - Aprausheva, N.
AU - Gorlach, I.
AU - Zhelnin, A.
AU - Sorokin, S.
PY - 2013
SP - 114
EP - 120
DO - 10.5220/0004395001140120