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.

• 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

Dialog

Label	Explanation	Data 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

Python

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

Name	Explanation	Data 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. 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 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. LOW—The density of points will be low, creating the fewest number of tie points. MEDIUM—The density of points will be medium, creating a moderate number of points. HIGH—The density of points will be high, creating the highest number of points.	String
distribution	Specifies whether the points will have regular or random distribution. RANDOM—Points will be generated randomly. Randomly generated points are better for overlapping areas with irregular shapes. REGULAR—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
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

Environments

Current Workspace, Parallel Processing Factor, Scratch Workspace