Skip to main content

Neural Network Combination by Fuzzy Integral for Robust Change Detection in Remotely Sensed Imagery

Abstract

Combining multiple neural networks has been used to improve the decision accuracy in many application fields including pattern recognition and classification. In this paper, we investigate the potential of this approach for land cover change detection. In a first step, we perform many experiments in order to find the optimal individual networks in terms of architecture and training rule. In the second step, different neural network change detectors are combined using a method based on the notion of fuzzy integral. This method combines objective evidences in the form of network outputs, with subjective measures of their performances. Various forms of the fuzzy integral, which are, namely, Choquet integral, Sugeno integral, and two extensions of Sugeno integral with ordered weighted averaging operators, are implemented. Experimental analysis using error matrices and Kappa analysis showed that the fuzzy integral outperforms individual networks and constitutes an appropriate strategy to increase the accuracy of change detection.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Hassiba Nemmour.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Nemmour, H., Chibani, Y. Neural Network Combination by Fuzzy Integral for Robust Change Detection in Remotely Sensed Imagery. EURASIP J. Adv. Signal Process. 2005, 413784 (2005). https://doi.org/10.1155/ASP.2005.2187

Download citation

Keywords and phrases

  • remote sensing
  • change detection
  • neural network
  • fuzzy integral