AM-65 - Geospatial data classification

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)