Pattern Analysis & Applications

, Volume 5, Issue 2, pp 210–220 | Cite as

Hierarchical Fusion of Multiple Classifiers for Hyperspectral Data Analysis

  • Shailesh Kumar
  • Joydeep Ghosh
  • Melba M. Crawford

Abstract:

Many classification problems involve high dimensional inputs and a large number of classes. Multiclassifier fusion approaches to such difficult problems typically centre around smart feature extraction, input resampling methods, or input space partitioning to exploit modular learning. In this paper, we investigate how partitioning of the output space (i.e. the set of class labels) can be exploited in a multiclassifier fusion framework to simplify such problems and to yield better solutions. Specifically, we introduce a hierarchical technique to recursively decompose a C-class problem into C_1 two-(meta) class problems. A generalised modular learning framework is used to partition a set of classes into two disjoint groups called meta-classes. The coupled problems of finding a good partition and of searching for a linear feature extractor that best discriminates the resulting two meta-classes are solved simultaneously at each stage of the recursive algorithm. This results in a binary tree whose leaf nodes represent the original C classes. The proposed hierarchical multiclassifier framework is particularly effective for difficult classification problems involving a moderately large number of classes. The proposed method is illustrated on a problem related to classification of landcover using hyperspectral data: a 12-class AVIRIS subset with 180 bands. For this problem, the classification accuracies obtained were superior to most other techniques developed for hyperspectral classification. Moreover, the class hierarchies that were automatically discovered conformed very well with human domain experts’ opinions, which demonstrates the potential of using such a modular learning approach for discovering domain knowledge automatically from data.

Key words: Binary hierarchical classifier; Fisher discriminant; Hyperspectral data; Hyperspectral feature extraction; Output space decompo-sition; Pattern recognition; Remote sensing 

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Copyright information

© Springer-Verlag London Limited 2002

Authors and Affiliations

  • Shailesh Kumar
    • 1
  • Joydeep Ghosh
    • 1
  • Melba M. Crawford
    • 2
  1. 1.Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USAUS
  2. 2.Center for Space Research, The University of Texas at Austin, Austin, TX, USAUS

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