Chapter 13. Land and Sea Surface Temperature Estimation by Support Vector Regression

  1. Dr Gustavo Camps-Valls B.Sc., Ph.D. professor member2 and
  2. Dr Lorenzo Bruzzone M.S., Ph.D. Postdoctoral Researcher Professor member Chair3
  1. Gabriele Moser and
  2. Sebastiano B. Serpico

Published Online: 4 NOV 2009

DOI: 10.1002/9780470748992.ch13

Kernel Methods for Remote Sensing Data Analysis

Kernel Methods for Remote Sensing Data Analysis

How to Cite

Moser, G. and Serpico, S. B. (2009) Land and Sea Surface Temperature Estimation by Support Vector Regression, in Kernel Methods for Remote Sensing Data Analysis (eds G. Camps-Valls and L. Bruzzone), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470748992.ch13

Editor Information

  1. 2

    Image Processing Laboratory (IPL) & Dept. Enginyeria Electrónica, Universitat de Valéncia, Spain

  2. 3

    Dept. Information Engineering and Computer Science, University of Trento, Italy

Author Information

  1. Dept. of Biophysical and Electronic Engineering (DIBE) and Interuniversity Research Center in Environmental Monitoring (CIMA) University of Genoa, Genoa, Italy

Publication History

  1. Published Online: 4 NOV 2009
  2. Published Print: 23 OCT 2009

ISBN Information

Print ISBN: 9780470722114

Online ISBN: 9780470748992

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Keywords:

  • land and sea surface temperature estimation by support vector regression;
  • LST - hydrological and meteorological models and satellite infrared remote sensing;
  • land surface temperature (LST) and sea surface temperature (SST) and role in environmental models;
  • split-window techniques (SWTs) in multiple TIR channels;
  • support vector classification (SVC);
  • pointwise statistical modelling of SVR error;
  • pixelwise probability distribution of SVR error;
  • SEVIRI instrument on board Meteosat Second Generation (MSG);
  • SVR framework methods - automating parameter-optimization processes;
  • generalization-error bound concepts used by PSB

Summary

This chapter contains sections titled:

  • Introduction

  • Previous work

  • Methodology

  • Experimental results

  • Conclusions

  • Acknowledgments

  • References