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 your ortho mapping project. For example, frame triangulation is performed for aerial images.
The block adjustment parameters for digital aerial imagery are described below. These parameters are used when computing tie points or ground control points (GCP) and when computing block adjustment.
For information about adjustment options for drone or scanned imagery, see Adjustment options for ortho mapping drone imagery or Adjustment options for ortho mapping scanned imagery, respectively.
Perform Camera Calibration
Automatic camera calibration computes and improves the camera’s geometric parameters, including interior orientation and lens distortion, while determining image orientation and image ground coordinates. If the camera has not been calibrated, select this option to improve the overall quality and accuracy of bundle block adjustment.
You can calibrate your camera during block adjustment to improve the camera's parameter accuracy; however, most high-quality digital cameras have been calibrated, in which case Perform Camera Calibration should not be checked. This is the default.
Note:
Higher in-strip and cross-strip aerial image overlap is recommended for better block adjustment and product generation results.Blunder Point Threshold
Tie points with a residual error greater than the Blunder Point Threshold value are not used in computing 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.
The values consist of three levels that are used in the tie point calculation algorithm to determine the number of images in the neighborhood to use. For example, when the accuracy is set to High, the algorithm uses a smaller neighborhood to identify matching features in the overlapping images.
| Level | Description |
|---|---|
Low | 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 in the point matching computation. |
Medium | Images have moderate location accuracy and small errors in sensor orientation (rotation of less than 5 degrees). The Harris algorithm is used in the point matching computation. This is the default. |
High | Images have high location accuracy and small errors in sensor orientation. This option is suitable for satellite imagery and aerial imagery that has been provided with exterior orientation data. The Harris algorithm is used in the point matching computation. |
Tie Point Similarity
Choose the tolerance level for matching tie points between image pairs.
| Level | Description |
|---|---|
Low | The similarity tolerance for the matching imagery pairs is low. This option 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. |
High | The similarity tolerance for the matching pairs is high. This option 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.
| Level | Description |
|---|---|
Low | The fewest number of tie points is produced. |
Medium | An intermediate number of tie points is produced. This is the default. |
High | A high number of tie points is produced. |
Tie Point Distribution
Choose whether the output 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—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.