Boosting, Bagging, and Consensus Based Classification of Multisource Remote Sensing Data

  • Gunnar Jakob Briem
  • Jon Atli Benediktsson
  • Johannes R. Sveinsson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2096)


The need to optimize the classification accuracy of remotely sensed imagery has led to an increasing use of Earth observation data with different characteristics collected from a variety of sensors from different parts of the electromagnetic spectrum. Combining multisource data is believed to offer enhanced capabilities for the classification of target surfaces. In the paper several single and multiple classifiers which are appropriate for classification of multisource remote sensing and geographic data are considered. The focus is on multiple classifiers: bagging algorithms, boosting algorithms, and consensus theoretic classifiers. These multiple classifiers have different characteristics. The performance of the algorithms in terms of accuracies is compared for a multisource remote sensing and geographic data set.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Gunnar Jakob Briem
    • 1
  • Jon Atli Benediktsson
    • 1
  • Johannes R. Sveinsson
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of IcelandReykjavikIceland

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