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Compute Tie Points

Summary

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.

Usage

  • 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.

Syntax

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

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

Mosaic Layer; Mosaic Dataset
out_control_points

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

Feature Class
similarity
(Optional)

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.
String
in_mask_dataset
(Optional)

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
out_image_features
(Optional)

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

Feature Class
density

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.
String
distribution

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.
String
location_accuracy

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.
String

Code sample

ComputeTiePoints example 1 (Python window)

This is a Python sample for the ComputeTiePoints tool.

import arcpy
arcpy.ComputeTiePoints_management("c:/workspace/BD.gdb/redQB", 
     "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