Neural Network Combination by Fuzzy Integral for Robust Change Detection in Remotely Sensed Imagery
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Neural Network Combination by Fuzzy Integral for Robust Change Detection in Remotely Sensed Imagery Hassiba Nemmour Signal Processing Laboratory, Faculty of Electronic and Computer Sciences, University of Sciences and Technology Houari Boumediene , 16111 Algiers, Algeria Email: [email protected]
Youcef Chibani Signal Processing Laboratory, Faculty of Electronic and Computer Sciences, University of Sciences and Technology Houari Boumediene , 16111 Algiers, Algeria Email: [email protected] Received 31 December 2003; Revised 5 December 2004 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. Keywords and phrases: remote sensing, change detection, neural network, fuzzy integral.
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INTRODUCTION
Analysis of multitemporal images of remote sensing is used for multiple purposes like environment monitoring and wide-area surveillance. These applications involve the identification of changes in land cover and land use practices. Hence, even, a pair of spatially registered images acquired on the same ground area at different times is analyzed to identify areas that have changed. Commonly, the comparison of independently produced classifications of data is used since it provides complete knowledge upon the change [1]. However, there are major problems associated with this technique. On one hand, its accuracy is critically dependent upon the two individual classifications. On the other hand, it does not allow the detection of subtle changes within a land cover class [2]. Recently, an alternative approach based on simultaneous classification of multitemporal data begins to be used to overcome these drawbacks and allow an automatic extraction of different kinds of change [3, 4]. To develop such a change detector, one can adopt statistical classifiers that are widely used in remote sensing such as the max-
imum likelihood. However, these algorithms are based on hard and commonly untenable assumptions about the data. Therefore, nonparametric classifiers such as neural networks and fuzzy classifiers are increasingly being used. Presently, we focus our attention o
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