Compute Control Points (Data Management)

Summary

Creates the control points between the mosaic dataset and the reference image. The control points can then be used in conjunction with tie points to compute the adjustments for the mosaic dataset.

Usage

  • For accurate control point results, use the High similarity option for the Similarity parameter.

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

  • The control points and tie points are then used within 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.

Parameters

LabelExplanationData Type
Input Mosaic Dataset

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

Mosaic Dataset; Mosaic Layer
Input Reference Images

The reference images that will be used to create control points for your mosaic dataset. If you have multiple images, create a mosaic dataset from the images and use the mosaic dataset as the reference.

Raster Layer; Raster Dataset; Image Service; Map Server; WMS Map; Mosaic Layer; Internet Tiled Layer; Map Server Layer
Output Control Points

The output control point table. This table will contain the control points that were created.

Feature Class
Similarity
(Optional)

Specifies the similarity level that will be used for matching tie points.

  • Low similarity —The 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 similarity —The similarity criteria for the matching points will be medium.
  • High similarity —The 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
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 density —The density of points will be low, creating the fewest number of tie points.
  • Medium point density —The density of points will be medium, creating a moderate number of points.
  • High point density —The 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 distribution —Points will be generated randomly. Randomly generated points are better for overlapping areas with irregular shapes.
  • Regular point distribution —Points 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
Area of Interest

Limit the area in which tie points are generated to only this polygon feature class.

Feature Layer
Image Location Accuracy
(Optional)

Specifies the keyword that describes the accuracy of the imagery.

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

arcpy.management.ComputeControlPoints(in_mosaic_dataset, in_reference_images, out_control_points, {similarity}, {out_image_feature_points}, density, distribution, area_of_interest, {location_accuracy})
NameExplanationData Type
in_mosaic_dataset

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

Mosaic Dataset; Mosaic Layer
in_reference_images

The reference images that will be used to create control points for your mosaic dataset. If you have multiple images, create a mosaic dataset from the images and use the mosaic dataset as the reference.

Raster Layer; Raster Dataset; Image Service; Map Server; WMS Map; Mosaic Layer; Internet Tiled Layer; Map Server Layer
out_control_points

The output control point table. This table will contain the control points that were created.

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
out_image_feature_points
(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
area_of_interest

Limit the area in which tie points are generated to only this polygon feature class.

Feature Layer
location_accuracy
(Optional)

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

ComputeControlPoints example 1 (Python window)

This is a Python sample for the ComputeControlPoints tool.

import arcpy
arcpy.ComputeControlPoints_management("c:/block/BD.gdb/redQB", 
     "c:/block/BD.gdb/redQB_tiePoints", "HIGH",
     "c:/block/BD.gdb/redQB_mask", "c:/block/BD.gdb/redQB_imgFeatures")
ComputeControlPoints example 2 (stand-alone script)

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

#compute control points

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

#compute control points using a mask 
mdName = "BD.gdb/redlandsQB"
in_mask = "BD.gdb/redlandsQB_mask"
out_controlPoint = "BD.gdb/redlandsQB_tiePoints"
out_imageFeature = "BD.gdb/redlandsQB_imageFeatures"

arcpy.ComputeControlPoints_management(mdName, out_controlPoint, 
     "HIGH", in_mask, out_imageFeature)

Licensing information

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

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