Classification of Remotely Sensed Images Using Neural-Network Ensemble and Fuzzy Integration
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.
KeywordsHide Node Back Propagation Neural Network Fuzzy Measure Neural Network Ensemble Overall Accuracy
- 4.Sugeno, M.: Fuzzy Measures and Fuzzy Integrals: A Survey. In: Fuzzy Automata and Decision Process, pp. 89–102. North Holland, Amsterdam (1977)Google Scholar
- 6.Sharkey, A., Tumer, K., Ghosh, J.: Linear and Order Statistics Combiners for Pattern Classification. In: Combining Artificial Neural Networks, May 1999, pp. 127–161. Springer, London (1999)Google Scholar