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Nonparametric Algorithms for Estimating the States of Natural Objects

  • A. V. LapkoEmail author
  • V. A. Lapko
Analysis and Synthesis of Signals and Images

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.

Keywords

pattern 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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Copyright information

© Allerton Press, Inc. 2018

Authors and Affiliations

  1. 1.Institute of Computational Modeling, Siberian BranchRussian Academy of SciencesKrasnoyarskRussia
  2. 2.Reshetnev Siberian State University of Science and TechnologyKrasnoyarskRussia

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