Assessing the accuracy of Landsat Thematic Mapper classification using double sampling
International Journal of Remote Sensing
By: M.A. Kalkhan, R.M. Reich, and T.J. Stohlgren
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Abstract
Double sampling was used to provide a cost efficient estimate of the accuracy of a Landsat Thematic Mapper (TM) classification map of a scene located in the Rocky Moutnain National Park, Colorado. In the first phase, 200 sample points were randomly selected to assess the accuracy between Landsat TM data and aerial photography. The overall accuracy and Kappa statistic were 49.5% and 32.5%, respectively. In the second phase, 25 sample points identified in the first phase were selected using stratified random sampling and located in the field. This information was used to correct for misclassification errors associated with the first phase samples. The overall accuracy and Kappa statistic increased to 59.6% and 45.6%, respectively.Double sampling was used to provide a cost efficient estimate of the accuracy of a Landsat Thematic Mapper (TM) classification map of a scene located in the Rocky Mountain National Park, Colorado. In the first phase, 200 sample points were randomly selected to assess the accuracy between Landsat TM data and aerial photography. The overall accuracy and Kappa statistic were 49.5 per cent and 32.5 per cent, respectively. In the second phase, 25 sample points identified in the first phase were selected using stratified random sampling and located in the field. This information was used to correct for misclassification errors associated with the first phase samples. The overall accuracy and Kappa statistic increased to 59.6 per cent and 45.6 per cent, respectively.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Assessing the accuracy of Landsat Thematic Mapper classification using double sampling |
| Series title | International Journal of Remote Sensing |
| Volume | 19 |
| Issue | 11 |
| Year Published | 1998 |
| Language | English |
| Publisher | Taylor & Francis Ltd |
| Publisher location | London, United Kingdom |
| Larger Work Type | Article |
| Larger Work Subtype | Journal Article |
| Larger Work Title | International Journal of Remote Sensing |
| First page | 2049 |
| Last page | 2060 |
| Google Analytic Metrics | Metrics page |