Compute Tie Points (Data Management)


Computes the tie points between overlapped mosaic dataset items. The tie points can then be used to compute the block adjustments for the mosaic dataset.


  • The tie points can be combined with control points using the Append Control Points tool.

  • The tie points and the optional control points are then used as the inputs for the Compute Block Adjustment tool.

  • If you have a mosaic dataset with many items, use caution when specifying the Output Image Features parameter, since your result may take a long time to process.


ComputeTiePoints(in_mosaic_dataset, out_control_points, {similarity}, {in_mask_dataset}, {out_image_features}, density, distribution, location_accuracy)
ParameterExplanationData Type

The input mosaic dataset that will be used to create tie points.

Mosaic Layer; Mosaic Dataset

The output control point table. The table will contain the tie points created by this tool.

Feature Class

Specifies the similarity level for matching tie points.

  • LOWThe similarity criteria for the two matching points will be low. This option will produce the most matching points, but some of the matches may have a higher level of error.
  • MEDIUMThe similarity criteria for the matching points will be medium.
  • HIGHThe similarity criteria for the matching points will be high. This option will produce the least number of matching points, but each matching will have a lower level of error.

A polygon feature class used to exclude areas you do not want in the computation of control points.

A field with a name of mask can control the inclusion or exclusion of areas. A value of 1 indicates that the areas defined by the polygons (inside) will be excluded from the computation. A value of 2 indicates the defined polygons (inside) will be included in the computation while areas outside of the polygons will be excluded.

Feature Layer

The output image feature points table. This will be saved as a polygon feature class. This output can be quite large.

Feature Class

The number of tie points to be created.

  • LOWSet the density of points to be low. This will create the fewest number of tie points.
  • MEDIUMSet the density of points to be medium. This will create a moderate number of points.
  • HIGHSet the density of points to be high. This will create the highest number of points.

Specifies whether the points will have regular or random distribution.

  • RANDOMPoints are generated randomly. Randomly generated points are better for overlapping areas with irregular shapes.
  • REGULARPoints are generated based on a fixed pattern. Points based on a fixed pattern use the point density to determine how frequently to create points.

Specifies the keyword that describes the accuracy of the imagery.

  • LOWImages have a large shift and a large rotation (> 5 degrees).The SIFT algorithm will be used in the point-matching computation.
  • MEDIUMImages have a medium shift and a small rotation (<5 degrees).The Harris algorithm will be used in the point-matching computation.
  • HIGHImages have a small shift and a small rotation.The Harris algorithm will be used in the point-matching computation.

Code sample

ComputeTiePoints example 1 (Python window)

This is a Python sample for the ComputeTiePoints tool.

import arcpy
     "c:/workspace/BD.gdb/redQB_tiePoints", "MEDIUM")
ComputeTiePoints example 2 (stand-alone script)

This is a stand-alone script sample for the ComputeTiePoints tool.

#compute tie points

import arcpy
arcpy.env.workspace = "c:/workspace"

#Compute tie points for a mosaic dataset
mdName = "BD.gdb/redlandsQB"
out_tiePoint = "BD.gdb/redlandsQB_tiePoints"

arcpy.ComputeTiePoints_management(mdName, out_tiePoint, "MEDIUM")

Licensing information

  • Basic: No
  • Standard: Yes
  • Advanced: Yes

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