Density-Based Unsupervised Classification for Remote Sensing *
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.
KeywordsRemote Sensing Landsat Rounded
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- 2.C.H.M. van Kemenade, J.A. La Poutre, and R.J. Mokken, “Density-based unsupervised classification for remote sensing”, Technical Report SEN-R9810 (also available via http://www.cwi.nl/~hlp/PAPERS/RS/dense98.ps), CWI, Amsterdam, the Netherlands, 1998.Google Scholar
- 3.J.A. Richards, Remote sensing digital image analysis, Springer-Verlag, Berlin, 1993.Google Scholar
- 4.D.W. Scott, Multivariate Density Estimation, Wiley series in probability and Mathematical Statistics, John Wiley & Sons, INC., New York, 1993.Google Scholar
- 5.B.W. Silverman, Density estimation for statistics and data analysis, Monographs on Statistics and Applied Probability, Chapman and Hall, London, 1986.Google Scholar