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- Create a raster map which contains additional ground truth information (such a map is also known as the test set). It is strongly advised that the test set raster map does not contain the same pixels as the sample set raster map from the training phase.
- Furthermore, the output raster map of the image classification is required.
- Then, perform a Cross with the ground truth map and the classified map to obtain a cross table.
- Open the cross table in a table window, and choose Confusion matrix from the View menu in the table window.
- If in the dialog box, you choose the ground truth map for the first column, and the classification results for the second column (i.e. the same as shown above), then the ground truth can be found in the rows of the confusion matrix, and the classification results will appear in the columns. You can read the explanation below without further changes.
- However, if in the dialog box, you choose the classification results as the first column, and the ground truth map for the second column (i.e. reverse as shown above), then the classification results can be found in the rows in the confusion matrix, and the ground truth will appear in the columns. You will then have to read 'columns' instead of 'rows', and 'rows' instead of 'columns' in the remainder of this topic. Furthermore you should read ACC (accuracy) instead of REL (reliability) and vice versa.
- unclass represents the Unclassified column,
- ACC represents the Accuracy column,
- REL represents the Reliability column.
- Rows correspond to classes in the ground truth map (or test set).
- Columns correspond to classes in the classification result.
- The diagonal elements in the matrix represent the number of correctly classified pixels of each class, i.e. the number of ground truth pixels with a certain class name that actually obtained the same class name during classification. In the example above, 440 pixels of 'forest' in the test set were correctly classified as 'forest' in the classified image.
- The off-diagonal elements represent misclassified pixels or the classification errors, i.e. the number of ground truth pixels that ended up in another class during classification. In the example above, 40 pixels of 'forest' in the test set were classified as 'bush' in the classified image.
- Off-diagonal row elements represent ground truth pixels of a certain class which were excluded from that class during classification. Such errors are also known as errors of omission or exclusion. For example, 50 ground truth pixels of 'crop' were excluded from the 'crop' class in the classification and ended up in the 'bare' class.
- Off-diagonal column elements represent ground truth pixels of other classes that were included in a certain classification class. Such errors are also known as errors of commission or inclusion. For example, 100 ground truth pixels of 'urban' were included in the 'bare' class by the classification.
- The figures in column Unclassified represent the ground truth pixels that were found not classified in the classified image.