Trainable method for predicting Characteristics of land surface objects

Alexander Murynin, Konstantin Gorokhovskiy, Vladimir Ignatiev

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

A new method for predicting characteristics of land surface objects has been proposed. The method is based on finding annual periodical patterns and comparison with a pattern obtained for year of observation. An example of the method application is considered. In the example authors propose, train and test a model for forecasting of crop yields based on multi-year remote observations of vegetation conditions in several regions of Russian Federation.

Original languageEnglish
Title of host publicationProceedings of the IADIS International Conference Computer Graphics, Visualization, Computer Vision and Image Processing 2013, CGVCVIP 2013
Pages119-125
Number of pages7
Publication statusPublished - 2013
Externally publishedYes
EventIADIS International Conference Computer Graphics, Visualization, Computer Vision and Image Processing 2013, CGVCVIP 2013, Part of the IADIS Multi Conference on Computer Science and Information Systems 2013, MCCSIS 2013 - Prague, Czech Republic
Duration: 22 Jul 201324 Jul 2013

Publication series

NameProceedings of the IADIS International Conference Computer Graphics, Visualization, Computer Vision and Image Processing 2013, CGVCVIP 2013

Conference

ConferenceIADIS International Conference Computer Graphics, Visualization, Computer Vision and Image Processing 2013, CGVCVIP 2013, Part of the IADIS Multi Conference on Computer Science and Information Systems 2013, MCCSIS 2013
Country/TerritoryCzech Republic
CityPrague
Period22/07/1324/07/13

Keywords

  • Forecasting
  • Image mining
  • Nonlinear regression
  • Remote sensing

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