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 paper we describe a non-parametric unsupervised classification method. The method uses biased sampling to obtain a learning sample with little noise. Next, density estimation based clustering is used to find the structure in the learning data. The method generates a non-parametric model for each of the classes and uses these models to classify the pixels in the image.
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|Software Engineering [SEN]|
|Organisation||Intelligent and autonomous systems|
van Kemenade, C.H.M, La Poutré, J.A, & Mokken, R.J. (1998). Density-based unsupervised classification for remote sensing. Software Engineering [SEN]. CWI.