Neural maps in remote sensing image analysis

  • Authors:
  • Thomas Villmann;Erzsébet Merényi;Barbara Hammer

  • Affiliations:
  • Klinik für Psychotherapie, Universität Leipzig, Karl-Tauchnitz-Str. 25, 04107 Leipzig, Germany;Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, MS 380 Houston, TX;Department of Mathematics/Computer Science, University of Osnabrück, Albrechtstraße 28, 49069 Osnabrück, Germany

  • Venue:
  • Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
  • Year:
  • 2003

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Abstract

We study the application of self-organizing maps (SOMs) for the analyses of remote sensing spectral images. Advanced airborne and satellite-based imaging spectrometers produce very high-dimensional spectral signatures that provide key information to many scientific investigations about, the surface and atmosphere of Earth and other planets. These new, sophisticated data demand new and advanced approaches to cluster detection, visualization, and supervised classification. In this article we concentrate on the issue of faithful topological mapping in order to avoid false interpretations of cluster maps created by an SOM. We describe several new extensions of the standard SOM, developed in the past few years: the growing SOM, magnification control, and generalized relevance learning vector quantization, and demonstrate their effect on both low-dimensional traditional multi-spectral imagery and ∼ 200-dimensional hyperspectral imagery.