Clustering and Unsupervised Classification

  • John A. Richards


The classification techniques treated in Chap. 8 all require the availability of labelled training data with which the parameters of the respective class models are estimated. As a result, they are called supervised techniques because, in a sense, the analyst supervises an algorithm’s learning about those parameters. Sometimes labelled training data is not available and yet it would still be of interest to convert remote sensing image data into a thematic map of labels. Such an approach is called unsupervised classification since the analyst, in principle, takes no part in an algorithm’s learning process. Several methods are available for unsupervised learning. Perhaps the most common in remote sensing is based on the use of clustering algorithms, which seek to identify pixels in an image that are spectrally similar. That is one of the applications of clustering treated in this chapter.


Cluster Algorithm Cluster Centre Spectral Domain Information Class Unsupervised Classification 
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.

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.ANU College of Engineering and Computer ScienceAustralian National UniversityCanberraAustralia

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