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)
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)
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)
AM-66 - Multi-layer feed-forward neural networks