NONPARAMETRIC ALGORITHMS FOR ESTIMATING THE STATES OF NATURAL OBJECTS
A. V. Lapko1,2, V. A. Lapko1,2
1Institute of Computational Modeling, Siberian Branch, Russian Academy of Sciences, 660036, Krasnoyarsk, Academgorodok, 50, building 44
2Reshetnev Siberian State University of Science and Technology, 660037, Krasnoyarsk, prosp. im. gazety “Krasnoyarskii rabochii”, 31
Keywords: распознавание образов, ядерная оценка плотности вероятности, выбор коэффициента размытости, решающее правило с градациями преимущества, дистанционное зондирование, состояния лесных массивов, pattern recognition, kernel estimation of the probability density, choice of the bandwidth, decision rule with advantage gradations, remote sensing, state of forest tracts
Abstract
Modifications of a nonparametric pattern recognition algorithm corresponding to the maximum likelihood criterion with additional decision functions are considered. The synthesis of the proposed algorithms is based on the analysis of the ratios of the estimates of the probability density distributions of random variables in classes and their functionals with input thresholds. The choice of the thresholds is determined by specific features of the classification problem. The results obtained are applied for assessing the states of forest tracts on the basis of remote sensing data.
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