Clustering and Unsupervised Classification
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.
KeywordsCluster Centre Agglomerative Hierarchical Cluster Unsupervised Classification Iterative Optimization Spectral Classis
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References for Chapter 9
- G.H. Ball and D.J. Hall, 1965: A Novel Method of Data Analysis and Pattern Classification. Stanford Research Institute, Menlo Park, California.Google Scholar
- D. J. Kelly, 1983: The Concept of a Spectral Class — A Comparison of Clustering Algorithms. M. Eng. Sc. Thesis. The University of New South Wales, Australia.Google Scholar
- P.A. Letts, 1978: Unsupervised Classification in The Aries Image Analysis System. Proc. 5th Canadian Symp. on Remote Sensing, 61-71.Google Scholar
- T.L. Phillips (Ed), 1973: LARSYS Version 3 Users Manual. Laboratory for Applications of Remote Sensing, Purdue University, West Lafayette.Google Scholar
- J. van Ryzin, 1977: Classification and Clustering. N.Y., Academic.Google Scholar
- R.C. Tryon and D.E. Bailey, 1970: Cluster Analysis, N.Y, McGraw-Hill.Google Scholar