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Adjustment options for ortho mapping satellite and aerial imagery

The parameters used in computing the block adjustment are defined in the Adjustment Options dialog box. The appropriate adjustment options are presented depending on the type of workspace defined when you set up your ortho mapping project. For example, RPC triangulation is performed for satellite images and frame triangulation is performed for aerial images.

The block adjustment settings specific for satellite imagery and digital aerial imagery are described below. These parameters are used in tie point or ground control point (GCP) computation, and computing the block adjustment.

For more information about adjustment options for drone and scanned imagery, see Adjustment options for drone and scanned imagery.

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.

Parameter settingDescription


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. The Harris algorithm will be used in the point matching computation.

Point Similarity

Choose the tolerance level for matching tie points between image pairs.

Parameter settingDescription


The similarity tolerance for the matching imagery pairs will be low. This option will produce the most matching pairs, but some of the matches may have a higher level of error.


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, but each matching pair will have a lower level of error.

Point Density

The relative number of tie points between image pairs to be created.

Parameter SettingDescription


This produces the fewest number of tie points.


This produces an intermediate number of tie points. This is the default.


This produces the most tie points.

Point Distribution

Determines 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—Generates points based on a fixed pattern.

Mask Polygon Features

A polygon feature class used to exclude areas you do not want used in 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) 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.

Maximum Residual

Tie points with a residual error greater than the Maximum Residual value will be not be used in computing the adjustment. The measurement unit of the residual is pixels.

Perform Camera Calibration

Aerial camera calibration is performed to identify and correct for image distortions induced by the sensor system. The camera's internal parameters, including focal length, principal point, and lens distortion parameters are used to produce a camera correction model. This information is used to compute the interior orientation, which is the relationship between the imaging plane and the sensor platform. For traditional aerial images collected with high performance metric cameras that are calibrated and have an associated calibration report, you do not need to perform camera calibration.

If you don't have a camera calibration report, or your camera parameters are not reliable, you can calibrate your camera during block adjustment to improve the camera parameter accuracy. The automatic camera calibration requires that your image collection has in-strip overlap of more than 60% overlap, and cross-strip overlap of more than 30%. Check on Perform Camera Calibration to compute the camera calibration. This is the default.

Higher in-strip and cross-strip aerial image overlap is recommended for better block adjustment and product generation results.

Compute ground control points

You can use a reference image to calculate GCPs. When choosing a reference image for GCP computation, make sure your reference image has good georeferencing quality in terms of geopositional accuracy and clarity, and the resolution is similar to your source imagery. For example, the default ArcGIS Online World Imagery Service may be a good reference for computing GCPs for your satellite data, but will likely not be a good reference for high resolution and highly accurate aerial imagery.

The adjustment options for Compute ground control points include the Reference Image which is the source image you want to base your ground control points on, and the parameter settings similar to those above:

  • Image Location Accuracy
  • Point Similarity
  • Point density
  • Point Distribution

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