|
|
Remote sensing imagery is becoming a popular and accessible tool in agriculture,
forestry, and rangeland management. Spectral classification of imagery provides
a means of estimating production and identifying potential problems, such as weed,
insect and disease infestations. Accuracy of classification is traditionally
based on ground truthing and summary statistics such as Cohen's Kappa.
Variability assessment and comparison of these quantities have been limited to
asymptotic procedures relying on large sample sizes and Gaussian distributions.
However, asymptotic methods fail to take into account the underlying distribution
of the classified data and may produce invalid inferential results. Bayesian
methodology is introduced to develop probability distributions for Cohen's
Conditional Kappa and misclassification rates which can subsequently be used for
image assessment and comparison. Techniques are demonstrated on a set of images
used in identifying a species of weed, yellow starthistle, at various spatial
resolutions and flying times.
All interested faculty, staff, and graduate students are invited to attend. |
|