Nonparametric Algorithms for Estimating the States of Natural Objects
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.
Keywordspattern recognition kernel estimation of the probability density choice of the bandwidth decision rule with advantage gradations remote sensing state of forest tracts
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- 1.S. M. Borzov and O. I. Potaturkin, “Efficiency of the Spectral-Spatial Classification of Hyperspectral Imaging Data,” Avtometriya 53 (1), 32–42 (2017) [Optoelectron., Instrum. Data Process. 53 (1), 26–34 (2017)].Google Scholar
- 2.S. M. Borzov and O. I. Potaturkin, “Classification of Hyperspectral Images with Different Methods of Training Set Formation,” Avtometriya 54 (1), 89–97 (2018) [Optoelectron., Instrum. Data Process. 54 (1), 76–82 (2018)].Google Scholar
- 3.I. V. Zen’kov, S. T. Im, A. V. Lapko, et al., Development and Application of Information Technologies for Studying Natural Resources of Siberian Territories on the Basis of Remote Sensing Data (SibGAU, Krasnoyarsk, 2017) [in Russian].Google Scholar
- 7.A. V. Lapko and V. A. Lapko, “Nonparametric Estimate of the Parzen-Type Probability Density with an Implicitly Defined Form of the Kernel Function,” Izmer. Tekhnika, No. 6, 14–17 (2016).Google Scholar
- 9.A. V. Lapko and V. A. Lapko, “Regression Estimation of the Multidimensional Probability Density and its Properties,” Avtometriya 50 (2), 50–56 (2014) [Optoelectron., Instrum. Data Process. 50 (2), 148–153 (2014)].Google Scholar
- 10.T. Duong, “Ks: Kernel Density Estimation and Kernel Discriminant Analysis in R,” J. Statist. Software 21 (7), 1–16 (2017).Google Scholar
- 16.A. V. Lapko and V. A. Lapko, “Analysis of Asymptotic Properties of Nonparametric Estimation of the Equation of the Separation Surface in a Two-Alternative Problem of Pattern Recognition,” Avtometriya 46 (3), 48–53 (2010) [Optoelectron., Instrum. Data Process. 46 (3), 243–247 (2010)].Google Scholar