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Author and Citation Info:
DiBiase, D., DeMers, M., Johnson, A., Kemp, K., Luck, A. T., Plewe, B., and Wentz, E. (2006). Geospatial data classification. The Geographic Information Science & Technology Body of Knowledge. Washington, DC: Association of American Geographers. (2nd Quarter 2016, first digital).
Learning Objectives:
Compare and contrast the assumptions and performance of parametric and non-parametric approaches to multivariate data classification
Describe three algorithms that are commonly used to conduct geospatial data classification
Explain the effect of including geospatial contiguity as an explicit neighborhood classification criterion
Compare and contrast the results of the neural approach to those obtained using more traditional Gaussian maximum likelihood classification (available in most remote sensing systems)
You are currently viewing an archived version of Topic . If updates or revisions have been published you can find them at .
DiBiase, D., DeMers, M., Johnson, A., Kemp, K., Luck, A. T., Plewe, B., and Wentz, E. (2006). Geospatial data classification. The Geographic Information Science & Technology Body of Knowledge. Washington, DC: Association of American Geographers. (2nd Quarter 2016, first digital).