Classification of Remotely Sensed Images Using Neural-Network Ensemble and Fuzzy Integration

  • G. Mallikarjun Reddy
  • B. Krishna Mohan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)

Abstract

An algorithm for fusing multiple remotely sensed image classifiers is addressed herein using fuzzy integral with error proportionate fuzzy measures. This method includes a procedure for calculating the λ-fuzzy measures which are adjusted depending on error correlation among the individual classifiers. Based on these fuzzy measures, the fuzzy integral is then used as non-linear function to search for maximum degree of agreement between multiple conflicting sources of evidence. Results obtained are used for decision making in classification problem. Experimental results on classification of remotely sensed images show that the performance of proposed multi-classifier method performs better than conventional method where fixed fuzzy measures are used.

Keywords

Hide Node Back Propagation Neural Network Fuzzy Measure Neural Network Ensemble Overall Accuracy 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • G. Mallikarjun Reddy
    • 1
  • B. Krishna Mohan
    • 2
  1. 1.Kanwal Rekhi School of Information TechnologyIndian Institute of Technology BombayPowai, MumbaiIndia
  2. 2.Centre of Studies in Resource EngineeringIndian Institute of Technology BombayPowai, MumbaiIndia

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