Combining classifiers using Dempster-Shafer evidence theory to improve remote sensing images classification

Classification system and textural features play increasingly an important role in remotely sensed images classification and many pattern recognition applications. In this work, we propose to fuse the information outputed by 3 well-known classifiers: Support Vector Machines (SVM), Neural Network (NN...

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Bibliographic Details
Main Authors: Mejdoubi, M., Aboutajdine, D., Kerroum, M. A., Hammouch, A.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Classifier Combination
Dempster-Shafer Theory
GMMFS algorithm
Image color analysis
Pattern recognition
Pixel
Remote sensing
remote sensing images
Support vector machines
Textural Feature
Training
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Summary:Classification system and textural features play increasingly an important role in remotely sensed images classification and many pattern recognition applications. In this work, we propose to fuse the information outputed by 3 well-known classifiers: Support Vector Machines (SVM), Neural Network (NN) and Parzen Window (PW). These classifiers were combined according to the Dempster-Shafer theory. The input textural feature used are selected according the GMMFS algorithm. The classification results show that the proposed method gives high performances than those of classifiers separately considered.
DOI:10.1109/ICMCS.2011.5945724