| Statistical Programs |
| College of Agriculture | University of Idaho |
| Seminar Announcement |
| "Applied Statistics in Agriculture" |
|
Comparing Binomial, Bootstrap, and Bayesian
Estimation Methods in Assessing the Agreement Between Classified
Images and Ground Truth Data
Presented By |
| Dr. Bahman Shafii |
|
Statistical Programs College of Agriculture University of Idaho |
|
Tuesday, Feb. 20 3:30 P. M. Room 62 College of Agriculture |
| The degree of agreement between classification and ground truth in remotely sensed data is often quantified with an error matrix and summarized using agreement measures such as Cohen's kappa. In the case of ground truth, however, the kappa statistic can be shown to be a transformation of the marginal proportions commonly referred to as omissional and commissional error rates. A more meaningful statistical interpretation of remote sensing results and less ambiguous conclusions can be obtained via direct utilization of the measures. Several estimation techniques have been suggested for these marginal proportions. In this study, we will develop the exact binomial, bootstrap, and Bayesian estimation methods and their corresponding empirical distributions. Results are demonstrated with reference to a study designed to evaluate the detectability of yellow hawkweed and oxeye daisy using multispectral digital imagery in Northern Idaho. |
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