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Extensions and Modifications of the Kohonen-SOM and Applications in Remote Sensing Image Analysis

  • Thomas Villmann
  • Erzsébet Merényi
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 78)

Summary

Utilization of remote sensing multi- and hyperspectral imagery has shown a rapid increase in many areas of economic and scientific significance over the past ten years. Hyperspectral sensors, in particular, are capable of capturing the detailed spectral signatures that uniquely characterize a great number of diverse surface materials. Interpretation of these very high-dimensional signatures, however, has proved an insurmountable challenge for many traditional classification, clustering and visualization methods. This chapter presents spectral image analyses with Self-Organizing Maps (SOMs). Several recent extensions to the original Kohonen SOM are discussed, emphasizing the necessity of faithful topological mapping for correct interpretation. The effectiveness of the presented approaches is demonstrated through case studies on real-life multi- and hyperspectral images.

Keywords

Receptive Field Hyperspectral Image Output Space Cinder Cone Hyperspectral Imagery 
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 2002

Authors and Affiliations

  • Thomas Villmann
  • Erzsébet Merényi

There are no affiliations available

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