The algorithm used by the Maximum Likelihood Classification tool is based on two principles:

### Example

The following example shows the classification of a multiband raster with three layers into five classes. The five classes are dry riverbed, forest, lake, residential/grove, and rangeland. An output confidence raster will also be produced. The input raster bands are displayed below.

The Maximum Likelihood Classification tool is used to classify the stack into five classes. The following settings were used:

Areas displayed in red are cells that have less than a 1 percent chance of being correctly classified. These cells are given the value NoData due to the 0.01 reject fraction used. The dry riverbed class is displayed as white, with the forest class as green, lake class as blue, residential/grove class as yellow, and rangeland as orange.

The list below is the value attribute table for the output confidence raster. It shows the number of cells classified with what amount of confidence. Value 1 has a 100 percent chance of being correct. There are 3,033 cells that were classified with that level of confidence. Value 5 has a 95 percent chance of being correct. There were 10,701 cells that have a 0.005 percent chance of being correct with a value of 14.

- The cells in each class sample in the multidimensional space are normally distributed.
- Bayes' theorem of decision making.

The Maximum Likelihood Classification tool is used to classify the stack into five classes. The following settings were used:

The classified raster appears as:`Input raster bands = "redlands"Input signature file = "wedit.gsg"Output classified raster = "mlclass_1"Reject fraction = "0.01"A priori probability weighting = "EQUAL"Input a priori probability file = "apriori_file_1"Output confidence raster = "reject_ras"`

Areas displayed in red are cells that have less than a 1 percent chance of being correctly classified. These cells are given the value NoData due to the 0.01 reject fraction used. The dry riverbed class is displayed as white, with the forest class as green, lake class as blue, residential/grove class as yellow, and rangeland as orange.

The list below is the value attribute table for the output confidence raster. It shows the number of cells classified with what amount of confidence. Value 1 has a 100 percent chance of being correct. There are 3,033 cells that were classified with that level of confidence. Value 5 has a 95 percent chance of being correct. There were 10,701 cells that have a 0.005 percent chance of being correct with a value of 14.

`Record VALUE COUNT1 1 30332 2 30613 3 91874 4 167175 5 373616 6 1364207 7 2695928 8 2508639 9 10500110 10 2359811 11 1119012 12 1154613 13 362114 14 10701`