An example is when the classified image identifies a pixel as asphalt, but the reference data identifies it as forest. The Total row shows the number of points that should have been identified as a given class, according to the reference data. The user’s accuracy is calculated by dividing the total number of classified points that agree with the reference data by the total number of classified points for that class. The data to compute this error rate is read from the rows of the table. User's accuracy is also referred to as Type 1 error. The user's accuracy column shows false positives, or errors of commission, where pixels are incorrectly classified as a known class when they should have been classified as something different. The following is an example of a confusion These accuracy rates range from 0 to 1, where 1 ( U_Accuracy column) and producer's accuracy ( P_Accuracy column) for each class, as well as an overall kappa statistic The confusion matrix table lists the user's accuracy dbf file located in your project or in the output folder you specified. ![]() If you want to examine the details of the error report, you can load the report into the Contents pane and open it. By hovering over the cells, you can see the confusion matrix results for each pairing of classes. Unlike the diagonal, the colored cells off the diagonal indicate the number of confused class values present in the confusion matrix. When you hover over each cell, the values for each accuracy and an F score are reported. The color along the diagonal ranges from light to dark blue, with darker blue indicating higher accuracy. The Producer Accuracy and User Accuracy values for all of the classes are indicated along the diagonal axis. Analyze the diagonalĪccuracy is represented from 0 to 1, with 1 being 100 percent accuracy. The output table is added to the Contents pane. The kappa score is also displayed at the bottom of the pane. Hover over a cell to see the Count, User Accuracy, Producer Accuracy, and FScore values. ![]() Once you run the tool, you see a graphical representation of your confusion matrix.
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