Combining classifiers using Dempster-Shafer evidence theory to improve remote sensing images classification
Published 1 April 2011
Mustapha Mejdoubi, Driss Aboutajdine, Mounir Ait Kerroum, Ahmed Hammouch
Citations4
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TL;DR
This work proposes to fuse the information outputed by 3 well-known classifiers : Support Vector Machines, Neural Network and Parzen Window according to the Dempster-Shafer theory to give high performances than those of classifiers separately considered.
Abstract
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.
Keywords
Computer ScienceEngineering
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