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Remote Sensing Digital Image Analysis pp 223–238Cite as

Clustering and Unsupervised Classification

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Abstract

The successful application of maximum likelihood classification is dependent upon having delineated correctly the spectral classes in the image data of interest. This is necessary since each class is to be modelled by a normal probability distribution, as discussed in Chap. 8. If a class happens to be multimodal, and this is not resolved, then clearly the modelling cannot be very effective.

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References for Chapter 9

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Author information

Authors and Affiliations

  1. Research School of Information Sciences and Engineering, The Australian National University, Canberra, ACT, 0200, Australia

    John A. Richards

  2. School of Electrical Engineering University College, The University of New South Wales, Australian Defence Force Academy, Campbell, ACT, 2600, Australia

    Xiuping Jia

Authors
  1. John A. Richards
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  2. Xiuping Jia
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© 1999 Springer-Verlag Berlin Heidelberg

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Richards, J.A., Jia, X. (1999). Clustering and Unsupervised Classification. In: Remote Sensing Digital Image Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-03978-6_9

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  • DOI: https://doi.org/10.1007/978-3-662-03978-6_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-03980-9

  • Online ISBN: 978-3-662-03978-6

  • eBook Packages: Springer Book Archive

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