Density-Based Unsupervised Classification for Remote Sensing *

  • Cees H. M. van Kemenade
  • Han La Poutre
  • Robert J. Mokken
Conference paper


Most image classification methods are supervised, and use a parametric model of the classes that have to be detected. The models of the different classes are trained by means of a set of training regions that usually have to be marked and classified by a human interpreter. Unsupervised classification methods are data-driven methods that do not use such a set of training samples. Instead these methods look for (repeated) structures in the data. In this chapter we describe a non-parametric unsupervised classification method, which uses biased sampling to obtain a learning sample with little noise. A density estimation based clustering is then used to find structures in the learning data. The method generated a non-parametric model for each of the classes and uses these models to classify the pixels in the image.


Remote Sensing Ground Cover Unsupervised Classification Hierarchical Cluster Method Spectral Space 
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 1999

Authors and Affiliations

  • Cees H. M. van Kemenade
    • 1
  • Han La Poutre
    • 1
  • Robert J. Mokken
    • 2
  1. 1.CWI, Centre for Mathematics and Computer ScienceAmsterdamthe Netherlands
  2. 2.Center for Computer Science in Organization and Management (CCSOM), Department of Statistics and Methodology, PSCWUniversity of AmsterdamAmsterdamthe Netherlands

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