ANALYSIS OF A CLASSIFICATION ERROR MATRIX USING CATEGORICAL DATA TECHNIQUES.
By: George H. Rosenfield and Katherine Fitzpatrick-Lins
Links
- The Publications Warehouse does not have links to digital versions of this publication at this time
- Download citation as: RIS | Dublin Core
Abstract
Summary form only given. A classification error matrix typically contains tabulation results of an accuracy evaluation of a thematic classification, such as that of a land use and land cover map. The diagonal elements of the matrix represent the counts corrected, and the usual designation of classification accuracy has been the total percent correct. The nondiagonal elements of the matrix have usually been neglected. The classification error matrix is known in statistical terms as a contingency table of categorical data. As an example, an application of these methodologies to a problem of remotely sensed data concerning two photointerpreters and four categories of classification indicated that there is no significant difference in the interpretation between the two photointerpreters, and that there are significant differences among the interpreted category classifications. However, two categories, oak and cottonwood, are not separable in classification in this experiment at the 0. 51 percent probability. A coefficient of agreement is determined for the interpreted map as a whole, and individually for each of the interpreted categories. A conditional coefficient of agreement for the individual categories is compared to other methods for expressing category accuracy which have already been presented in the remote sensing literature.
| Publication type | Conference Paper |
|---|---|
| Publication Subtype | Conference Paper |
| Title | ANALYSIS OF A CLASSIFICATION ERROR MATRIX USING CATEGORICAL DATA TECHNIQUES. |
| ISBN | 081860588X |
| Year Published | 1984 |
| Language | English |
| Publisher | IEEE |
| Publisher location | New York, NY, USA |
| First page | 386 |
| Conference Title | Proceedings - PECORA 9: Spatial Information Technologies for Remote Sensing Today and Tomorrow. |
| Conference Location | Sioux Falls, ND, USA |
| Google Analytic Metrics | Metrics page |