| 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 |
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.
Parameters
arcpy.rm.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
This is a Python sample for the AnalyzeControlPoints function.
import arcpy
arcpy.AnalyzeControlPoints_rm(
"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 )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.AnalyzeControlPoints_rm(mdName, in_controlPoint,
out_coverage, out_overlap, in_mask, 5)Environments
Licensing information
- Basic: No
- Standard: Yes
- Advanced: Yes