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Hybrid Hierarchical Classifiers for Hyperspectral Data Analysis

  • Goo Jun
  • Joydeep Ghosh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5519)

Abstract

We propose a hybrid hierarchical classifier that solves multi-class problems in high dimensional space using a set of binary classifiers arranged as a tree in the space of classes. It incorporates good aspects of both the binary hierarchical classifier (BHC) and the margin tree algorithm, and is effective over a large range of (sample size, input dimensionality) values. Two aspects of the proposed classifier are empirically evaluated on two hyperspectral datasets: the structure of the class hierarchy and the classification accuracies. The proposed hybrid algorithm is shown to be superior on both aspects when compared to other binary classification trees, including both the BHC and the margin tree algorithm.

Keywords

Radial Basis Function Hybrid Algorithm Hyperspectral Image Hyperspectral Data Okavango Delta 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Goo Jun
    • 1
  • Joydeep Ghosh
    • 1
  1. 1.Department of Electrical and Computer EngineeringThe University of Texas at AustinAustinUSA

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