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Variance approximations for assessments of classification accuracyAuthor(s): R. L. Czaplewski
Source: Res. Pap. RM-316. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station. 29 p.
Publication Series: Research Paper (RP)
Station: Rocky Mountain Forest and Range Experiment Station
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DescriptionVariance approximations are derived for the weighted and unweighted kappa statistics, the conditional kappa statistic, and conditional probabilities. These statistics are useful to assess classification accuracy, such as accuracy of remotely sensed classifications in thematic maps when compared to a sample of reference classifications made in the field. Published variance approximations assume multinomial sampling errors, which implies simple random sampling where each sample unit is classified into one and only one mutually exclusive category with each of two classification methods. The variance approximations in this paper are useful for more general cases, such as reference data from multiphase or cluster sampling. As an example, these approximations are used to develop variance estimators for accuracy assessments with a stratified random sample of reference data.
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CitationCzaplewski, R. L. 1994. Variance approximations for assessments of classification accuracy. Res. Pap. RM-RP-316. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station. 29 p.
KeywordsKappa, remote sensing, photo-interpretation, stratified random sampling, cluster sampling, multiphase sampling, multivariate composite estimation, reference data, agreement
- Statistical properties of measures of association and the Kappa statistic for assessing the accuracy of remotely sensed data using double sampling
- Variance estimates and confidence intervals for the Kappa measure of classification accuracy
- Calibration of remotely sensed proportion or area estimates for misclassification error
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