Compute Tie Points (Data Management)

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 value, since the result may take a long time to process.

Parameters

LabelExplanationData Type
Input Mosaic Dataset

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

Mosaic Layer; Mosaic Dataset
Output 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 that will be used for matching tie points.

  • Low similarityThe 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.
  • Medium similarityThe similarity criteria for the matching points will be medium.
  • High similarityThe similarity criteria for the matching points will be high. This option will produce the fewest matching points, but each match will have a lower level of error.
String
Input Mask
(Optional)

A polygon feature class used to exclude areas that will not be included 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
Output 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
Point Density

Specifies the number of tie points to be created.

  • Low point densityThe density of points will be low, creating the fewest number of tie points.
  • Medium point densityThe density of points will be medium, creating a moderate number of points.
  • High point densityThe density of points will be high, creating the highest number of points.
String
Point Distribution

Specifies whether the points will have regular or random distribution.

  • Random point distributionPoints will be generated randomly. Randomly generated points are better for overlapping areas with irregular shapes.
  • Regular point distributionPoints will be generated based on a fixed pattern. Points based on a fixed pattern use the point density to determine how frequently to create points.
String
Image Location Accuracy

Specifies the keyword that describes the accuracy of the imagery.

  • Low image location accuracyImages have a large shift and a large rotation (> 5 degrees).The SIFT algorithm will be used in the point-matching computation.
  • Medium image location accuracyImages have a medium shift and a small rotation (<5 degrees).The Harris algorithm will be used in the point-matching computation.
  • High image location accuracyImages have a small shift and a small rotation.The Harris algorithm will be used in the point-matching computation.
String
Additional Options
(Optional)

Additional options for the adjustment engine. The options are only used by third-party adjustment engines.

Value Table

arcpy.management.ComputeTiePoints(in_mosaic_dataset, out_control_points, {similarity}, {in_mask_dataset}, {out_image_features}, density, distribution, location_accuracy, {options})
NameExplanationData 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 that will be used 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 fewest matching points, but each match will have a lower level of error.
String
in_mask_dataset
(Optional)

A polygon feature class used to exclude areas that will not be included 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

Specifies the number of tie points to be created.

  • LOWThe density of points will be low, creating the fewest number of tie points.
  • MEDIUMThe density of points will be medium, creating a moderate number of points.
  • HIGHThe density of points will be high, creating the highest number of points.
String
distribution

Specifies whether the points will have regular or random distribution.

  • RANDOMPoints will be generated randomly. Randomly generated points are better for overlapping areas with irregular shapes.
  • REGULARPoints will be 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
options
[options,...]
(Optional)

Additional options for the adjustment engine. The options are only used by third-party adjustment engines.

Value Table

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

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