Available with Advanced license.
The parameters used to compute the block adjustment are defined in the Adjust window. The available adjustment options depend on the type of workspace defined when you set up your ortho mapping project. For example, RPC or polynomial transformation options are available for satellite images.
The block adjustment settings specific for satellite imagery are described below. These parameters are used in tie point or ground control point (GCP) computation, and computing the block adjustment.
Three types of transformations are available for adjusting a mosaic dataset.
- RPC—The Rational Polynomial Coefficients 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 flat area.
Blunder Point Threshold
Tie points with a residual error greater than the Blunder Point Threshold value will not be used to compute the adjustment. The measurement unit of the residual is pixels.
Image Location Accuracy
The inherent positional accuracy of your imagery depends on the sensor viewing geometry, type of sensor, and level of processing. Positional accuracy is usually described as part of the imagery deliverable. Choose the keyword that best describes the accuracy of your imagery.
Images have a large shift and a large rotation (more than 5 degrees). The scale invariant feature transform (SIFT) algorithm will be used in the point matching computation.
Images have a medium amount of shift and a small rotation (less than 5 degrees). The Harris algorithm will be used in the point matching computation. This is the default.
Images have a small shift and a small rotation. This option is suitable for satellite imagery that has been provided with exterior orientation data. The Harris algorithm will be used in the point matching computation.
Tie Point Similarity
Choose the tolerance level for matching tie points between image pairs.
The similarity tolerance for the matching imagery pairs will be low. This option will produce the most matching pairs, although some of the matches may have a higher error associated with them.
The similarity tolerance for the matching pairs will be medium. This is the default.
The similarity tolerance for the matching pairs will be high. This option will produce the least number of matching pairs, although each matching pair will have lower error.
Tie Point Density
The relative number of tie points between image pairs to be created.
Produces the fewest number of tie points.
Produces an intermediate number of tie points. This is the default.
Produces the most tie points.
Tie Point Distribution
Determines whether the output tie points will 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.
- Regular—Generates points based on a fixed pattern.
Mask Polygon Features
A polygon feature class can be used to exclude areas when computing tie points.
In the feature class attribute table, the mask field controls the inclusion or exclusion of areas for computing tie points. A value of 1 indicates that the areas inside the polygons will be excluded from the computation. A value of 2 indicates that the areas inside the polygons will be included in the computation, while areas outside the polygons will be excluded.