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Land Cover Classification from MODIS Satellite Data Using Probabilistically Optimal Ensemble of Artificial Neural Networks

  • Kenneth J. Mackin
  • Eiji Nunohiro
  • Masanori Ohshiro
  • Kazuko Yamasaki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4253)

Abstract

Terra and Aqua, 2 satellites launched by the NASA-centered international Earth Observing System project, house MODIS (Moderate Resolution Imaging Spectroradiometer) sensors. Moderate resolution remote sensing allows the quantifying of land surface type and extent, which can be used to monitor changes in land cover and land use for extended periods of time. In this paper, we propose applying a probabilistically optimal ensemble technique, based on fault masking among individual classifier for N-version programming. We create an optimal ensemble of artificial neural networks and use the majority voting result to predict land surface cover from MODIS data. We show that an optimal ensemble of neural networks greatly improves the classification error rate of land cover type.

Keywords

Land Cover Land Cover Type Land Cover Classification Moderate Resolution Image Spectroradiometer Classification Error Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kenneth J. Mackin
    • 1
  • Eiji Nunohiro
    • 1
  • Masanori Ohshiro
    • 2
  • Kazuko Yamasaki
    • 2
  1. 1.Department of Information SystemsTokyo University of Information SciencesChibaJapan
  2. 2.Department of Enviromental InformationTokyo University of Information SciencesChibaJapan

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