Identification of cloud fields by the nonparametric algorithm of pattern recognition from normalized video data recorded with the AVHRR instrument

Konstantin T. Protasov; Tatyana Y. Pushkareva; Evgeny S. Artamonov

doi:10.1117/12.454467

31 January 2002 Identification of cloud fields by the nonparametric algorithm of pattern recognition from normalized video data recorded with the AVHRR instrument

Konstantin T. Protasov, Tatyana Y. Pushkareva, Evgeny S. Artamonov

Author Affiliations +

Proceedings Volume 4539, Remote Sensing of Clouds and the Atmosphere VI; (2002) https://doi.org/10.1117/12.454467
Event: International Symposium on Remote Sensing, 2001, Toulouse, France

Abstract

The problem of cloud field recognition from the NOAA satellite data is urgent for solving not only meteorological problems but also for resource-ecological monitoring of the Earth's underlying surface associated with the detection of thunderstorm clouds, estimation of the liquid water content of clouds and the moisture of the soil, the degree of fire hazard, etc. To solve these problems, we used the AVHRR/NOAA video data that regularly displayed the situation in the territory. The complexity and extremely nonstationary character of problems to be solved call for the use of information of all spectral channels, mathematical apparatus of testing statistical hypotheses, and methods of pattern recognition and identification of the informative parameters. For a class of detection and pattern recognition problems, the average risk functional is a natural criterion for the quality and the information content of the synthesized decision rules. In this case, to solve efficiently the problem of identifying cloud field types, the informative parameters must be determined by minimization of this functional. Since the conditional probability density functions, representing mathematical models of stochastic patterns, are unknown, the problem of nonparametric reconstruction of distributions from the leaning samples arises. To this end, we used nonparametric estimates of distributions with the modified Epanechnikov kernel. The unknown parameters of these distributions were determined by minimization of the risk functional, which for the learning sample was substituted by the empirical risk. After the conditional probability density functions had been reconstructed for the examined hypotheses, a cloudiness type was identified using the Bayes decision rule.

Citation Download Citation

Konstantin T. Protasov, Tatyana Y. Pushkareva, and Evgeny S. Artamonov "Identification of cloud fields by the nonparametric algorithm of pattern recognition from normalized video data recorded with the AVHRR instrument", Proc. SPIE 4539, Remote Sensing of Clouds and the Atmosphere VI, (31 January 2002); https://doi.org/10.1117/12.454467

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