Analyze Control Points (Data Management Tools)

Summary

Analyzes the control point coverage and identifies the areas that need additional control points to improve the block adjust result.

The tool will check each image and provide the following:

The number of control points for each image
The percentage of image covered by the control points (point distribution)
The overlap areas
The number of control points within overlap areas

Usage

You can specify a mask to either exclude or include certain areas.
Specify a minimum overlap area so you do not end up with small slivers to analyze.

Label	Explanation	Data type
Input Mosaic Dataset	The input mosaic dataset against which to analyze the control points.	Mosaic Dataset; Mosaic Layer
Input Control Points	The input control point feature class. It is normally created from the Compute Tie Points or the Compute Control Points tool.	Feature Layer
Output Control Point Coverage Feature Class	A polygon feature class output that contains the control point coverage and the percentage of the area within the corresponding image.	Feature Class
Output Overlap Feature Class	A polygon feature class output that contains all the overlap areas between images.	Feature Class
Input Mask (Optional)	A polygon feature class used to exclude areas that you do not want in the analysis of the control points computation. The `mask` field 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
Minimum Overlap Area (Optional)	Specify the minimum percent that the overlap area must be, in relation to the image. Areas that are lower than the specified percent threshold will be excluded from the analysis. Ensure that you do not have areas that are too small; otherwise, you will have small slivers being analyzed.	Double
Maximum Overlap Level (Optional)	The maximum number of images that can be overlapped when analyzing the control points. For example, if there are four images in your mosaic dataset, and a maximum overlap value of 3 was specified, then there are ten different combinations that will appear in the Overlap Window. If the four images were named i1, i2, i3, and i4, the ten combinations that would appear are [i1, i2, i3], [i1 i2 i4], [i1 i3 i4], [i2 i3 i4], [i1, i2], [i1, i3], [i1, i4], [i2, i3], [i2, i4], and [i3, i4].	Long

arcpy.management.AnalyzeControlPoints(in_mosaic_dataset, in_control_points, out_coverage_table, out_overlap_table, {in_mask_dataset}, {minimum_area}, {maximum_level})

Name	Explanation	Data type
in_mosaic_dataset	The input mosaic dataset against which to analyze the control points.	Mosaic Dataset; Mosaic Layer
in_control_points	The input control point feature class. It is normally created from the Compute Tie Points or the Compute Control Points tool.	Feature Layer
out_coverage_table	A polygon feature class output that contains the control point coverage and the percentage of the area within the corresponding image.	Feature Class
out_overlap_table	A polygon feature class output that contains all the overlap areas between images.	Feature Class
in_mask_dataset (Optional)	A polygon feature class used to exclude areas that you do not want in the analysis of the control points computation. The `mask` field 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
minimum_area (Optional)	Specify the minimum percent that the overlap area must be, in relation to the image. Areas that are lower than the specified percent threshold will be excluded from the analysis. Ensure that you do not have areas that are too small; otherwise, you will have small slivers being analyzed.	Double
maximum_level (Optional)	The maximum number of images that can be overlapped when analyzing the control points. For example, if there are four images in your mosaic dataset, and a maximum overlap value of 3 was specified, then there are ten different combinations that will appear in the Overlap Window. If the four images were named i1, i2, i3, and i4, the ten combinations that would appear are [i1, i2, i3], [i1 i2 i4], [i1 i3 i4], [i2 i3 i4], [i1, i2], [i1, i3], [i1, i4], [i2, i3], [i2, i4], and [i3, i4].	Long

Code sample

AnalyzeControlPoints example 1 (Python window)

This is a Python sample for the AnalyzeControlPoints function.

import arcpy
arcpy.management.AnalyzeControlPoints(
     "c:/BD/BD.gdb/redQB", "c:/BD/BD.gdb/redQB_tiePts",
     "c:/BD/BD.gdb/out_coverage", "c:/BD/BD.gdb/out_overlap",
     "c:/BD/BD.gdb/mask", 5 )

AnalyzeControlPoints example 2 (stand-alone script)

This is a Python script sample for the AnalyzeControlPoints function.

#analyze control points
import arcpy
arcpy.env.workspace = "c:/workspace"

#analyze the control points using a mask
mdName = "BD.gdb/redlandsQB"
in_controlPoint = "BD.gdb/redlandsQB_tiePoints"
out_coverage = "BD.gdb/out_overage"
out_overlap = "BD.gdb/out_overlap"
in_mask = "BD.gdb/mask"

arcpy.management.AnalyzeControlPoints(mdName, in_controlPoint,
     out_coverage, out_overlap, in_mask, 5)

Environments

Current Workspace, Scratch Workspace

Licensing information