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

Dialog

Label	Explanation	Data 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 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
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 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
Image Location Accuracy	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
Additional Options (Optional)	Additional options for the adjustment engine. The options are only used by third-party adjustment engines.	Value Table

Python

arcpy.management.ComputeTiePoints(in_mosaic_dataset, out_control_points, {similarity}, {in_mask_dataset}, {out_image_features}, density, distribution, location_accuracy, {options})

Name	Explanation	Data 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. 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
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. 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
location_accuracy	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
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")

Environments

Current Workspace, GPU ID, Parallel Processing Factor, Processor Type, Scratch Workspace

Licensing information

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