Available for an ArcGIS organization with the ArcGIS Reality license.
The parameters used in computing the block adjustment are defined in the Adjust window. The available adjustment options depend on the type of workspace you defined when you set up the Reality mapping project. For example, RPC or polynomial transformation options are available for satellite images.
Block adjustment
The block adjustment parameters for satellite imagery are described below. These parameters are used when computing tie points or ground control points (GCPs) and when computing a block adjustment.
Transformation type
Three types of transformations are available for adjusting a mosaic dataset comprised of satellite imagery.
- RPC—The Rational Polynomial Coefficients (RPC) will be used for the transformation. This is used for satellite imagery that contains RPC information within the metadata. This is the default.
- POLYORDER1—A first-order polynomial (affine) is used in the block adjustment computation.
- POLYORDER0—A zero-order polynomial is used in the block adjustment computation. This is commonly used when your data is in a flat area.
Reproject tie points
A part of the adjustment process includes computing and displaying each tie point at its correct 2D map location. This is an optional step that only supports the visual analysis of tie points with the 2D map view. Following adjustment, the Reproject Tie Points option in the Manage Tie Points drop-down menu must be used.
Note:
When working with large projects with more than 1,000 images, this step can be skipped to reduce adjustment processing duration, without any adverse impact to the adjustment quality.
Advanced Options
The Advanced Options section provides additional settings that can be used to optimize the adjustment process. A description of each setting is given below.
Image location accuracy
The inherent positional accuracy of the imagery depends on the sensor viewing geometry, type of sensor, and level of processing. Positional accuracy is typically described as part of the imagery deliverable. Choose the keyword that best describes the accuracy of the imagery.
The values consist of three settings that are used in the tie point calculation to determine the size of the search radius and the image matching algorithm used. For example, when the accuracy is set to High, the Harris algorithm used in conjunction with a smaller neighborhood to identify matching features in the overlapping images.
| Setting | Description |
|---|---|
Low | This option generates the largest number of tie points and should be used in scenarios where the terrain imaged is rolling or mountainous. This option is also used for images have poor location accuracy and large errors in sensor orientation (rotation of more than 5 degrees). The scale invariant feature transform (SIFT) algorithm is used, which has a large pixel search range to support point matching computation. |
Medium | This option generates a moderate number of tie points and should be used in scenarios where the images have moderate location accuracy and small errors in sensor orientation (rotation of less than 5 degrees). The Harris algorithm is used with a search range of approximately 800 pixels to support the point matching computation. This is the default setting. |
High | The High option generates the least number of tie points and should be used in scenarios where images have high location accuracy and small errors in sensor orientation. The Harris algorithm is used with a small search range to support point matching computation. |
Tie point similarity
Choose the tolerance level for matching tie points between image pairs.
| Setting | Description |
|---|---|
Low | The similarity tolerance for the matching imagery pairs is low. This setting produces the most matching pairs, but some of the matches may have a higher level of error. |
Medium | The similarity tolerance for the matching pairs is medium. This is the default setting. |
High | The similarity tolerance for the matching pairs is high. This setting produces the least number of matching pairs, but each matching pair has a lower level of error. |
Tie point density
Choose the relative number of tie points to be computed between image pairs.
| Setting | Description |
|---|---|
Low | The fewest number of tie points is produced. |
Medium | An intermediate number of tie points is produced. This is the default setting. |
High | A high number of tie points is produced. |
Tie point distribution
Choose whether the output points have a regular or random distribution.
- Random—Points are generated randomly. Randomly generated points are better for overlapping areas with irregular shapes. This is the default setting.
- Regular—Points are generated based on a fixed pattern.
Mask polygon features
Use a polygon feature class to exclude areas you do not want used when computing tie points.
In the attribute table of the feature class, the mask field controls the inclusion or exclusion of areas for computing tie points. A value of 1 indicates that the areas defined by the polygons (inside) are excluded from the computation. A value of 2 indicates that the areas defined by the polygons (inside) are included in the computation and areas outside of the polygons are excluded.
Note:
Ensure that statistics are generated for each image or item in the image collection prior to performing an adjustment. This will improve the tie point generation process. Statistics can be generated using the Build Pyramids and Statistics geoprocessing tool.