Manifold Learning for Multi-classifier Systems via Ensembles

  • Authors:

  • Melba Crawford;Wonkook Kim

  • Affiliations:

  • Laboratory for Applications of Remote Sensing, Purdue University,;Laboratory for Applications of Remote Sensing, Purdue University,

  • Venue:
  • MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
  • Year:
  • 2009

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Abstract

Statistical classification of hyperspectral data is challenging because the inputs are high in dimension, while the quantity of labeled data is typically limited. The resulting classifiers are often unstable and have poor generalization. Nonlinear manifold learning algorithms assume that the original high dimensional data actually lie on a low dimensional manifold defined by local geometric differences between samples. Recent research has demonstrated the potential of these approaches for nonlinear dimension reduction and representation of high dimensional observations. Nonlinear scattering phenomena associated with processes observed in remote sensing data suggest that these may be useful for analysis of hyperspectral data. However, computational requirements limit their applicability for classification of remotely sensed data. Multi-classifier systems potentially provide a means to exploit the advantages of manifold learning through decomposition frameworks, while providing improved generalization. This paper reports preliminary results obtained from an ensemble implementation of Landmark Isomap in conjunction with a kNN classifier. The goal is to achieve improved generalization of the classifier in analysis of hyperspectral data in a dynamic environment with limited training data. The new method is implemented and applied to Hyperion hyperspectral data collected over the Okavango Delta of Botswana.