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Compute Control Points

  • Summary
  • Usage
  • Syntax
  • Code sample
  • Environments
  • Licensing information

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.

Syntax

ComputeControlPoints(in_mosaic_dataset, in_reference_images, out_control_points, {similarity}, {out_image_feature_points}, density, distribution, area_of_interest, {location_accuracy})
ParameterExplanationData 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; MapServer; 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 for matching tie points.

  • LOW —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 —The similarity criteria for the matching points will be medium.
  • HIGH —The 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
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

The number of tie points to be created.

  • LOW —Set the density of points to be low. This will create the fewest number of tie points.
  • MEDIUM —Set the density of points to be medium. This will create a moderate number of points.
  • HIGH —Set 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.

  • RANDOM —Points are generated randomly. Randomly generated points are better for overlapping areas with irregular shapes.
  • REGULAR —Points 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
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.

  • LOW —Images have a large shift and a large rotation (> 5 degrees).The SIFT algorithm will be used in the point-matching computation.
  • MEDIUM —Images have a medium shift and a small rotation (<5 degrees).The Harris algorithm will be used in the point-matching computation.
  • HIGH —Images 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)

Environments

  • Current Workspace
  • Parallel Processing Factor
  • Scratch Workspace

Licensing information

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

Related topics

  • An overview of the Raster toolset
  • Georeferencing a raster automatically
  • Fundamentals of georeferencing a raster dataset
  • Register Raster

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