The classify operation performs a multi-spectral image classification according to training pixels in a sample set. The following classification methods can be used: Box classifier, Minimum distance, Minimum Mahalanobis distance, Maximum Likelihood, and Maximum Likelihood with Prior Probabilities.
Dialog box options:
Sample set: |
Select an input sample set. Open the list box and select the desired sample set, or drag a sample set directly from the Catalog into this box. |
Classification method: |
Select a classification method: Box Classifier, Minimum Distance, Minimum Mahalanobis Distance, Maximum Likelihood or Prior Probability. For more information, see Classify : functionality. |
Multiplication factor: |
For the Box Classifier method, type a multiplication factor, usually larger than 1. The multiplication factor allows you to make boxes drawn around class means on the basis of their standard deviations a bit wider. |
Threshold distance: |
For the Minimum Distance, Minimum Mahalanobis distance, Maximum Likelihood and Prior Probability classification methods:
When, for a pixel to be classified, the spectral distance towards a class mean:
Tips:
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Table: |
For the Prior Probability classification method: select the table which contains the column with the prior probability values. The table must use the same class domain as the sample set. |
Column: |
For the Prior Probability classification method: select the column with the prior probability values. Ideally, the sum of probabilities equals 1. When this is not the case, the program will internally normalize the probability values. |
Output raster map: |
Type a name for the output raster map that will contain the outcome of the classification. |
Description: |
Optionally, type a description for the output map. The description will appear in the status bar of the Main window when moving the mouse pointer over the map in a Catalog, and in the title bar of a map window when the output map is displayed. If no description is supplied, the output map will use its own definition as description. |
When you click the Show button, the dependent output map will be defined, calculated and shown. When you click the Define button, the dependent output map will only be defined; if necessary the map will be calculated later, for instance when the map is opened to be displayed.
Tip:
To assess the accuracy of the classification, you can prepare a confusion matrix. For more information, see How to calculate a confusion matrix.
See also: