Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Neural Network Combination by Fuzzy Integral for Robust Change Detection in Remotely Sensed Imagery Hassiba Nemmou | EURASIP Journal on Applied Signal Processing 2005 14 2187-2195 2005 Hindawi Publishing Corporation 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 hnemmour@ Youcef Chibani Signal Processing Laboratory Faculty of Electronic and Computer Sciences University of Sciences and Technology Houari Boumediene 16111 Algiers Algeria Email ychibani@ 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. 1. 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
