### Classification

The Classification is a interface used for vector data and raster data either. The basic principle of this type of classification is grouping the data following the logic of some selected method.
It is accessible through:
Layer Explorer > Right Click over a Layer-name > Classification...
Layer > Classification...
If the user selects a layer with vector representation, the interface for vector classification will be shown, if the data is raster type, the interface for raster classification will be shown.

#### Vectorial

The available methods in Terralib are:

- Unique Values: It groups the values using the unique value algorithm.

- Equal Steps: It groups the values defined by a range of a attribute using the equal steps algorithm.

- Quantil: It groups the values defined by a range of a attribute using the quantil algorithm.

- Standard Deviation: It groups the values defined by a range of a attribute using the standard deviation algorithm.

To execute this operation is required the following steps:

1. Select the method for classification.
2. Select the attribute to be used for data classification. The attributes listed may vary depending on selected method of classification.
3. Set the number of slices to be created in the classification.
4. Sets the precision value.
5. Set the color ramp.
6. The "Apply" button will populate the table with slices generated.

Example of vectorial classification:
Input Data:
The result of this operation is shown in the figure below.

#### Raster

The available methods in Terralib are:

- Categorize: The transformation of continuous values to distinct values.

- Interpolation: The transformation of continuous values to a number of values.

To execute this operation is required the following steps:

1. Select the band to be applied the classification.
2. Set the minimum value. The suggested value is the minimum value found in the selected band.
3. Set the maximum value. The suggested value is the maximum value found in the selected band.
4. Select the method for classification.
5. Set the number of slices to be created in the classification.
6. Defines how data is classified.
7. Set the color ramp.
8. The "Apply" button will populate the table with slices generated.

Example of raster classification:
Input Raster:
The result of this operation using Categorize method is shown in the figure below.
The result of this operation using Interpolation method is shown in the figure below.