Chapter

Optical Remote Sensing

Volume 3 of the series Augmented Vision and Reality pp 147-170

Date:

Decision Fusion of Multiple Classifiers for Vegetation Mapping and Monitoring Applications by Means of Hyperspectral Data

  • Karoly Livius BakosAffiliated withDepartment of Electronics, Telecommunication and Remote Sensing Laboratory, University of Pavia Email author 
  • , Prashanth Reddy MarpuAffiliated withDepartment of Electronics, Telecommunication and Remote Sensing Laboratory, University of Pavia
  • , Paolo GambaAffiliated withDepartment of Electronics, Telecommunication and Remote Sensing Laboratory, University of Pavia

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Abstract

In this chapter, we introduce methodologies for fusion of multiple classifiers and a neural network architecture for mapping vegetation by means of remote sensing imagery. It is very normal that different classification schemes yield slightly different results for different classes. This effect is even more prominent in vegetation mapping applications due to the inconsistent spectral signatures of the vegetation classes. We study the possibility of combining the results of different classifiers by considering the best results for individual classes to produce an improved classification result. We propose two types of methodologies, one which uses only the classification result and the other which uses the class membership values produced by the weak classifiers. A comparison is also done with the simple majority voting scheme of the multiple classifiers. Our experiments clearly show the improvement of classification accuracy using the proposed fusion techniques.

Keywords

Hyperspectral Classification Neural Network Decision tree Ensemble