A Hierarchical Multiclassifier System for Hyperspectral Data Analysis

  • Shailesh Kumar
  • Joydeep Ghosh
  • Melba Crawford
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1857)


Many real world classification problems involve high dimensional inputs and a large number of classes. Feature extraction and modular learning approaches can be used to simplify such problems. In this paper, we introduce a hierarchical multiclassifier paradigm in which a C- class problem is recursively decomposed into C- 1 two-class problems. A generalized modular learning framework is used to partition a set of classes into two disjoint groups called meta-classes. The coupled problem 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 architecture was used to classify 12 types of landcover from 183-dimensional hyperspectral data. The classification accuracy was significantly improved by 4 to 10% relative to other feature extraction and modular learning approaches. Moreover, the class hierarchy that was automatically discovered conformed very well with a human domain expert–s opinion, which demonstrates the potential of such a modular learning approach for discovering domain knowledge automatically from data.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Shailesh Kumar
    • 1
  • Joydeep Ghosh
    • 1
  • Melba Crawford
    • 2
  1. 1.Laboratory of Artificial Neural Systems, Department of Electrical and Computer EngineeringThe University of Texas at AustinAustinUSA
  2. 2.Center for Space ResearchThe University of Texas at AustinAustinUSA

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