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Evolutionary Approach to Discovery of Classification Rules from Remote Sensing Images

  • Jerzy Korczak
  • Arnaud Quirin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2611)

Abstract

In this article a new method for classification of remote sensing images is described. For most applications, these images contain voluminous, complex, and sometimes noisy data. For the approach presented herein, image classification rules are discovered by an evolution-based process, rather than by applying an a priori chosen classification algorithm. During the evolution process, classification rules are created using raw remote sensing images, the expertise encoded in classified zones of images, and statistics about related thematic objects. The resultant set of evolved classification rules are simple to interpret, efficient, robust and noise resistant. This evolution-based approach is detailed and validated based on remote sensing images covering not only urban zones of Strasbourg, France, but also vegetation zones of the lagoon of Venice.

Keywords

Genetic Algorithm Hyperspectral Image Classification Rule Spectral Interval Mixed Pixel 
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 2003

Authors and Affiliations

  • Jerzy Korczak
    • 1
  • Arnaud Quirin
    • 1
  1. 1.Université Louis PasteurStrasbourgFrance

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