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Consistency Measure of Multiple Classifiers for Land Cover Classification by Remote Sensing Image

  • Peijun Du
  • Guangli Li
  • Wei Zhang
  • Xiaomei Wang
  • Hao Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5519)

Abstract

Nowadays, multiple classifier system is widely used for land cover classification by remote sensing imagery. The performance of combined classifier is closely related to the selection of member classifiers, so it is necessary to analyze the diversity and consistency of member classifiers. In our study, consistency measures are studied and experimented from three levels: general consistency measure, binary prior measure and consistency of errors, and the result shows that it is feasible to find the effective set of member classifiers by some consistency measures. In land cover classification by remotely sensed classifiers, we can select optimal member classifiers by integrating different consistency criterions.

Keywords

consistency measure multiple classifier combination land cover classification remote sensing weighted count of errors and correct results (WCEC) 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Peijun Du
    • 1
  • Guangli Li
    • 1
  • Wei Zhang
    • 1
  • Xiaomei Wang
    • 1
  • Hao Sun
    • 1
  1. 1.Department of Remote Sensing and Geographical Information ScienceChina University of Mining and TechnologyXuzhou CityP.R. China

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