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How Create Signatures works

Available with Spatial Analyst license.

The signature file generated by the Create Signatures tool is a statistical description of the classes derived from the samples identified on the input raster or feature sample data. The file consists of two sections:

  • The general information for all classes, such as the number of layers, input raster names, and number of classes.
  • Signature statistics for each class, which consist of the number of samples and the means and covariance matrices.

The tool creates a signature file to be used as the input for other multivariate analysis tools. For example, the Maximum Likelihood Classification tool performs a maximum likelihood classification that requires both the class mean vectors and covariance matrices from a signature file.

Example

The signature file produced by Create Signatures begins with a header, which is commented out. The header retains the inputs used to create the signature file. The class names are optional and can be entered through the Sample field or they can be added to the resulting signature file using any text editor.

Compute covariance option

The following example shows a signature file. The input is a multiband raster named redlands. The sample data is a raster redzone5, and has five sample classes. The Compute covariance matrices option has been left with the default enabled setting (COVARIANCE).

  • Settings used in the Create Signatures tool dialog box:

    Input raster bands : redlands

    Input raster or feature sample data : redzone5

    Sample field : "Value"

    Output signature file : z5red.gsg

    Compute covariance matrices : on

The output signature file is listed below:

# Signatures Produced by ClassSig from Zone-Grid redsamp5 and Stack redlands
# Number of selected grids
/*        3
# Layer-Number        Grid-name
/*        1            redlands3
/*        2            redlands1
/*        3            redlands2

# Type  Number of Classes  Number of Layers  Number of Parametric Layers
  1     5                  3                 3
#  ----------------------------------------------------------------------

# Class ID    Number of Cells    Class Name
  1           654                sand
# Layers   1           2           3
# Means 
         170.4908    155.7569    161.9419
# Covariance
1        292.6546    182.3661    186.2583
2        182.3661    127.8076    139.3009
3        186.2583    139.3009    196.3029
# ---------------------------------------------------------------

# Class ID    Number of Cells    Class Name
  2           585                urban
# Layers   1           2           3
# Means 
         104.5009     92.4410     92.0513
# Covariance
1        384.6580    552.1828    389.0496
2        552.1828   1378.6750    863.5595
3        389.0496    863.5595    772.2063
# ---------------------------------------------------------------

# Class ID    Number of Cells    Class Name
  3           783                forest
# Layers   1           2           3
# Means
          27.0026    174.3768     72.7931
# Covariance
1        241.0818    -14.6301    293.7806
2        -14.6301    764.2914    221.4054
3        293.7806    221.4054    527.0799
# ---------------------------------------------------------------

# Class ID    Number of Cells    Class Name
  4           951                water
# Layers   1           2           3
# Means
           1.1504      0.0515      0.0873
# Covariance
1          7.2753      3.9638      6.4848
2          3.9638      2.5247      4.0702
3          6.4848      4.0702      6.5724
# -----------------------------------------------------------------

# Class ID    Number of Cells     Class Name
  5           969                 agri_field
# Layers   1           2           3
# Means 
          32.4675    232.7781     85.4149
# Covariance
1        423.1004   -684.8693    324.1354
2       -684.8693   1271.6315   -509.0008
3        324.1354   -509.0008    366.1232

Means only option

If the covariance matrices are not required, disable the Compute covariance matrices option (MEAN_ONLY). Following is the signature file from the same data as above, but without calculating the covariance matrices:

Input raster bands : redlandsInput raster or feature sample data : redzone5Sample field : "Value"Output signature file : z5red.gsgCompute covariance matrices : off

The output signature file is listed below:

# Number of selected grids
/*        3
# Layer-Number        Grid-name
/*        1            redlands3
/*        2            redlands1
/*        3            redlands2

# Type  Number of Classes  Number of Layers  Number of Parametric Layers
  1     5                  3                 3
#  ----------------------------------------------------------------------

# Class ID    Number of Cells    Class Name
  1           654                sand
# Layers   1           2           3
# Means 
         170.4908    155.7569    161.9419
# ---------------------------------------------------------------

# Class ID    Number of Cells    Class Name
  2           585                urban
# Layers   1           2           3
# Means 
         104.5009     92.4410     92.0513
# ---------------------------------------------------------------

# Class ID    Number of Cells    Class Name
  3           783                forest
# Layers   1           2           3
# Means
          27.0026    174.3768     72.7931
# ---------------------------------------------------------------

# Class ID    Number of Cells    Class Name
  4           951                water
# Layers   1           2           3
# Means
           1.1504      0.0515      0.0873
# -----------------------------------------------------------------

# Class ID    Number of Cells     Class Name
  5           969                 agri_field
# Layers   1           2           3
# Means 
          32.4675    232.7781     85.4149

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