Parameter descriptions
Send Email
If necessary, you can set up CATALYST Enterprise to send an email notification on job start and job completion.
With this check box selected, an email message is sent to each address specified in the Email Addresses box after the job starts and on completion.
You can specify one or more addresses, and each must be separated by a comma or a semi-colon. The email address of the user currently logged in displays by default.
Scene Folder
The path and name of the folder containing RAW satellite scenes to ingest.
This parameter is required so the raw satellite data can be ingested into the working PCIDSK (.pix) format. The path must point to a folder containing raw imagery scenes as acquired from the satellite vendor. The folder must not contain a mixture of sensor types; rather, keep multiple sensor types separate, as the module is capable of traversing nested folders.
It is recommended that you run a separate job for each set of sensor data, and specify different output folders for each.
This parameter is mandatory.
Output Folder
The path and name of the folder to which to write the output linked PCIDSK files.
The module outputs PCIDSK files for each image imported from the input folder. If the input folder contains multispectral (MS) and panchromatic (PAN) images, the module creates output PCIDSK files for each image.
If GCPs were collected automatically, the module creates a text file for each image containing the GCPs collected ; these text files indicate the accuracy of each collected GCP and how well each GCP fits the existing math model. The text files also provide details on those images that contain automatically collected GCPs and those that do not.
The text files generated by the module for collected GCPs are stored in the same folder as the imported PCIDSK files.
The module automatically generates an OrthoEngine project file for each scene in which GCPs were collected automatically. To facilitate quality-assurance processes, it also generates a single OrthoEngine project file that includes the GCPs for all processed scenes. The merged file, FinalRefinedGCPs.prj is located in the specified output folder.
Ingest Method
Select whether to link or import the data specified in the scene folder.
- Automatic: Links or imports the data based on the sensor.
- Import: Creates from the file in its raw vendor format a PCIDISK file that contains the input scene.
This method may take longer to ingest the data; however, it improves the overall performance of the workflow.
Important: The Import method requires twice the amount of disk space, because it duplicates the pixel data. - Link: Creates a PCIDISK file that points to the input scene.
This method ingests data very quickly; however, subsequent steps of the workflow may take longer to complete.
Build Pyramids
This check box controls whether to build image pyramids (raster overviews) for the ingested scenes.
Building pyramids improves performance in some subsequent processing steps. They can use the pyramids instead of reading all the pixels. However, building pyramids can cause the ingest job to take longer to complete. If immediately after ingestion you intend only to create orthorectified images, you need not build pyramids.
When to turn on pyramids:
- View or edit the data in: CATALYST Professional Focus
- Perform some tasks in: CATALYST Professional OrthoEngine
- Run any of the aerial triangulation jobs:
For more information on pyramids, see the Pyramid module.
Overwrite Results
Select this check box to overwrite the existing output files, if any exist. If this check box is left clear, and an output file exists in the relevant folder, the status of the job displays a message informing you of the existence and name of the output file. The message is also written to the event log of the job.
Ingest Stereo Data
When selected, this check box ingests stereo data, if any exists in the scene folder.
You can use this parameter in conjunction with the Tri-Stereo Look Directions parameter to select only the specific view or views you want to ingest.
Tri-Stereo Look Directions
Available only when the Ingest Stereo Data check box is selected, and when the data is tri-stereo, such as ZY-3 scenes, select from the list the specific view or views you want to ingest.
- All (Nadir, Forward, Backward)
- Forward, Backward
- Nadir, Forward
- Nadir, Backward
- Nadir
- Forward
- Backward
Ingest Multispectral
Selected by default, this check box controls whether to ingest multispectral data, if any exists in the scene folder.
Ingest Panchromatic
Selected by default, this check box controls whether to ingest panchromatic data, if any exists in the scene folder.
Ingest Pansharpened
Selected by default, this check box controls whether to ingest pansharpened data, if any exists in the scene folder.
Ingest Other
To ingest other types of data, if any exists in the scene folder, select this check box.
Math Model Filter
The math model to use for collection of ground control points (GCPs) and, subsequently, orthorectification.
- Automatic: available in data-ingest-related modules only, such as the Data Ingest and GCP Collection module. With this option selected, any and all supported math models is added to the output file for each scene. Typically, only the selected math model is added to the output files.
- Rational Function Model (RPC): uses the Rational Polynomial Coefficients (RPCs) that are distributed with the raw imagery. With most modern sensors, this is the most robust model to use, as the distributed RPCs are very well calibrated (accurate). Therefore, fewer GCPs are required, generally, and positional improvements are not constrained to areas within the outer bounds of the GCPs collected. Other abbreviations for the Rational Function Model include RF, RFMODEL, RFI, RF0, RF1, and RF2.
- Toutin's Satellite Orbital Model (Rigorous): CATALYST Enterprise uses the Toutin rigorous math-model solution, which calculates the position and orientation of the sensor at the time of image capture from an orbital or ephemeris segment. When this information has been identified, it can then be used to accurately account for known distortions in the image. After the sensor orientation and position have been calculated, this information can be used to drive other processes, such as orthorectification. Other abbreviations for Toutin's Satellite Orbital Model include SAT, RIG, SRIT, and TOUTIN.
The module first attempts to use the selected math model. If required information is missing from the user-defined math model, the module automatically tries to use another math-model option, and displays a warning message.
RF Math Model Order
When using the Rational Function with satellite imagery, you can modify the RF math model to better agree with collected ground control points (GCPs) by selecting the correct RF math-model order.
- Order 0: allows x,y translations
- Order 1: allows x,y translations with rotation
- Order 2: allows x,y translations with rotation and increased warping
Typically, performing a first-order transformation is best, except when the GCPs are not well distributed. If your GCPs are clustered together, a first-order transformation may introduce new and significant errors in the image away from the GCPs. If your GCPs are not well distributed, you will probably obtain better results with the zero-order transformation.
Source Background Type
The method to use to determine which pixels in the source image to process as background (NoData) pixels. In general, if a pixel is considered NoData, the module processes it in a specific manner.
If the Any option or the All option is selected, a value must be specified for the Source Background Value parameter.
-
File Metadata, else None: reads the NoData value from the input-file metadata. The module first checks for the file-level metadata tag NO_DATA_VALUE in the source raster. If the tag is present, this value is used as a default for all channels in the file. Next, the module checks for channel-level NoData tags; if one is found, the channel-level value overrides the file-level value for that channel.
If there are channel-level NoData tags, but no file-level tag, a pixel is considered as NoData if each of the channels with a NoData tag corresponds to its NoData value. In this case, channels without a NoData tag are ignored when identifying background pixels.
If the file does not contain NoData tags, all pixels in the source image are considered valid.
- None: all pixels in the source image are to be considered valid
- All: if the pixel value in all channels matches the values provided for the Source Background Value parameter, the pixel is considered background (NoData).
- Any: if the pixel value in any channel matches the value provided for the Source Background Value parameter, the pixel is considered background (NoData).
For specific examples, see the Source Background Value parameter description.
Source Background Value
- All
- Any
The source background value is provided as either a single number (applied to all channels) or as a pixel "stack" (a comma-delimited list of values). If a pixel stack is provided, but the number of values does not equal the number of channels, the list is truncated or the last value is repeated as necessary. The background values provided is truncated to the range allowed by the source image data type.
-
Source Background Type set to All and Source Background Value set to 0: a pixel is considered background if all three channels are zero.
- Source Background Type set to Any and Source Background Value set to 0: a pixel is considered background if any of the three channels is zero
- Source Background Type set to All and Source Background Value set to 128,0,0: a pixel is considered background if its value is exactly 128 for the first channel and zero for the second and third channels
- Source Background Type set to All and Source Background Value set to 128,0,0,245: a pixel is considered background if its value is exactly 128 for the first channel and zero for the second and third channels. The value 245 is ignored.
- Source Background Type set to Any and Source Background Value set to 128,0: a pixel is considered background if the value in channel 1 is 128 OR if the value in channel 2 or 3 is zero.
- Source Background Type set to Any and Source Background Value set to 1032,0: a pixel is considered background if the value in channel 1 is 255 OR if the value in channel 2 or 3 is zero. The first value is truncated to the range allowed for 8U data.
Master Matching Channel
The channel from the input image to use for matching when collecting ground control points (GCPs). If no value is specified for this parameter, channel 1 is used, by default.
DEM Source
The name of a single digital elevation model (DEM) file or a folder containing one or more DEM tiles.
- Name of a single DEM file; for example, C:\data\DEM\dem.pix
- Name of a folder containing one or more DEM tiles and an associated index.txt file; for example, C:\data\DEM
- Name of an index file; for example, C:\data\DEM\index.txt
The index.txt file lists the DEM files contained in the specified folder and provides information describing each DEM tile. The information in the DEM index file supersedes other DEM parameters in the module; all other DEM-related parameters are ignored. For more information about the format of the index.txt file and specific requirements for the individual DEM tiles, see Format of the DEM index file.
When the value of DEM Source is the name of an existing folder, the module searches that folder for a file named index.txt, and a set of DEM raster tiles. The index.txt file contains a single vector channel that lists the DEM files contained in the specified folder and provides information describing each DEM tile.
If no value is specified for this parameter, the module uses the default global DEM installed with CATALYST Enterprise (gmted2010).
Reference Images
The path of a single reference image or a folder containing multiple reference images to be used for automatic collection of ground control points (GCP). Alternatively, you can enter a comma-delimited list of raster-image file names.
To use multiple GDB-supported geocoded reference images for automatic GCP collection, you can specify a reference-image folder (GDB-compatible) and an associated (GDB-compatible) digital elevation model for automatic GCP collection. The specified folder can contain multiple reference images to use for collecting GCPs.
The value of Reference Images is an image file that has been orthorectified previously for use with to automatic GCP collection. The GCPs collected from one or more of the reference images are stored in a GCP segment of the PCISDK image. The module also creates an OrthoEngine project that contains the same GCPs for quality- assurance (QA) tasks and manual editing.
When collecting GCPs using reference images, the module searches for ORTHO_X_ACCURACY and ORTHO_Y_ACCURACY metadata tags in the images. When such tags are present, the module uses those values to determine the accuracy of each GCP, thereby weighting the value of that point against others. The ORTHO_X_ACCURACY and ORTHO_Y_ACCURACY metadata tags are set in the Index PIX File Creator module. If no metadata tags are found in the reference image, the GCP accuracy is calculated to be half of the resolution of the reference image.
Reference Channel for Matching
The channel from reference image to use for matching when collecting ground control points (GCPs). Channel 1 is the default.
Sampling Method
The method to use to create ground control point (GCP) samples from the source imagery.
- Stereo Candidates: uses the GCP stereo candidates created by the GCP Reference Imagery Preparation module. When this option is selected, the GCP Samples and Search Radius parameters are ignored.
- Grid: sample points are generated automatically using sample points distributed evenly across the overlap region.
- Susan: sample points are generated automatically using the Susan corner detection algorithm
- The Grid option creates candidates in a grid-like pattern. This option does not preprocess the image, so it tends to be faster. However, it also finds fewer matches, because the grid point might fall on a featureless flat patch somewhere in the image that cannot be matched to anything in the overlapping reference images.
- The Susan option finds the candidates by running a corner-detection algorithm on the image, which looks for corner-like features to use as candidates.
When collecting GCPs, the Grid option is recommended because the Susan option finds candidates on building corners that may not be represented in the digital elevation model (DEM), leading to GCPs with higher residuals due to height errors.
GCP Samples
The maximum number of ground control points (GCPs) to collect per reference image. For example, when a raw scene overlaps four reference images and you specify the value of this parameter as 50, the maximum number of GCPs collected is four times that value for a total of 200 (50 x 4 = 200).
If you are using the Grid option, available values are:
- num_samples: sample points are generated automatically using sample points distributed evenly across the overlap region. The overlap region is divided into a grid, and the candidates are placed in the center of the grid cells. num_samples defines the number of samples to use. The size of the grid cells is chosen automatically, based on the number of samples requested. This is the default value.
- num_samples,margin: sample points are generated automatically using sample points distributed evenly across the overlap region. The distribution is similar to the previous Grid option, but the points are distributed starting at the edges of the overlap rather than in the center of the grid cells, which improves the chances of finding matches at the edge of the image. The margin value controls the minimum distance, in pixels, between the edge of the image and the placement of the candidates. Candidates that are too close to the image edge may not be matched properly; however, adjusting the margin value may alleviate this.
If using the Susan option, available values are:
- num_samples: sample points are generated automatically using the Susan corner-detection algorithm. num_samples defines the number of samples to use.
Matching Algorithm
The algorithm to use for automated point matching.
- Fast Fourier Transform Phase: when two images have a relative shift between them, the result is a phase difference in the frequency domain. Fast Fourier Transform Phase (FFTP) determines the shift between images using this phase difference.
- Normalized Cross-Correlation: this method finds the relative shift between two images by finding the one that produces the maximum cross-correlation coefficient of the gray values in the images.
When the two images being matched have similar gray values and appearances, Normalized Cross-Correlation (NCC) generally produces acceptable results. When there is a rotation or image-size error in the initial math models, NCC may produce better matching results than FFTP. Typically, this method also generates faster results, because the template size that NCC uses is smaller than that used by FFTP.
For more consistently accurate results, FFTP is recommended. This method uses a larger template size than NCC and, because it works in the frequency domain, it looks at the patterns of details in the image rather than the gray values in a small neighborhood, which NCC uses. This makes FFTP more robust than NCC when there is a large difference in brightness between images or when a major land-use change has occurred between the images. FFTP also allows for a better match between images of the same area from different sensors or spectral bands.
Collection Strategy
Number of passes in which to collect ground control points (GCPs) from reference images.
- Automatic: determines the number of passes based on the input data.
- Two Pass collects GCPs in two passes.
The first pass uses a coarse strategy, and the second uses a fine. When the input images are inaccurate, a two-pass strategy is recommended.
- One Pass (Fine): collects GCPs in one pass and uses a fine matching strategy.
When the input data is geometrically close to the reference data, and for making final adjustments, a fine strategy is recommended.
While a fine strategy is the most accurate at collecting GCPs, it is slower than a coarse strategy.
- One Pass (Coarse): collects GCPs in one pass and uses a coarse matching strategy.
When the accuracy of the input data is of poor quality, and to manually run two or more passes, a coarse strategy is recommended.
A coarse strategy adjusts the input data such that it is geometrically close to the reference imagery without too much overhead.
While a coarse strategy is faster than fine at collecting GCPs, it is less accurate.
Search Radius
The distance in the x and y directions from a starting location on the reference image over which the search for the best match with a fixed point on the input image is conducted.
The search radius is an estimation of error with the raw image's positional information and the digital elevation model (DEM) accuracy. If you know that your image is accurate to within 80 meters, and your DEM is accurate to within 200 meters, set the search radius to 280 meters. A larger search radius requires more processing time, because more locations are evaluated to determine the best match for a ground control point (GCP).
If this parameter is not specified, the function uses a default search radius based on the resolution of input data.
Minimum Score
The threshold value that controls whether a candidate ground control point (GCP) is accepted as a GCP or rejected. This parameter specifies the minimum-match quality that is considered acceptable, with 1.0 indicating a perfect match.
When using the Fast Fourier Transform Phase matching algorithm, this value is converted internally to a minimum acceptable phase-shift peak value.
When using the Normalized Cross-Correlation matching algorithm, this value specifies the minimum match score value required to accept a local match between the input and reference images as a GCP. The default value is 0.75.
GCP Priority
With this feature, you can set the priority of processing for one or more of your specific data aspects: your reference imagery, chip database, reference vectors, or GCPs you imported.
Each data aspect—reference imagery, chip database, reference vectors, or imported GCPs—has a GPC Priority parameter. The one with the highest selected value has the highest priority during processing. For example, if you set the GCP priority for your reference imagery to High, and the remaining aspects to Medium, the reference imagery is processed with the highest priority. Similarly, if you set the GCP priority for your reference imagery to Medium, and the remaining aspects to Low, the reference imagery is processed with the highest priority.
The Locked option retains the GCPs generated during the GCP collection process. GCP collection has two phases: collection and refinement. In the refinement phase, two separate subprocesses occur: GCP distribution and GCP refinement. During GCP distribution, the GCP priority applies. During the second phase, however, the GCP priority does not apply. Use this option when you want to retain the integrity of GCPs from a particular data aspect.
You can also set the GCP priority of more than one aspect to use the same value. That is, you can perhaps set a value of High to your reference imagery and your imported GCPs, as applicable.
Chip Database File
The path and file name of the chip database to use for automatically collecting ground control points (GCPs).
The module collects the GCPs from the chip database, and then automatically saves them to a GCP segment of the PCIDSK image associated with the GCP.
To improve the spatial accuracy of the collected GCPs, the chip database should contain high-quality image chips from a comparable sensor taken in similar conditions and be well-distributed over all of the scenes to be corrected.
Chip Channels
The number of the channels in the chip database to use for automated ground control point (GCP) matching.
Chip Matching Algorithm
The algorithm used for automated ground control point (GCP) matching.
- Fast Fourier Transform Phase
- Normalized Cross-Correlation
Chip Search Radius
The distance in the x and y directions from a starting location on the reference image over which the search for the best match with a fixed point on the input image is conducted.
The search radius is an estimation of error with the raw image's positional information and the accuracy of the digital elevation model (DEM). For example, if you know your image is accurate to within 80 meters and your DEM is accurate to within 200 meters, set the search radius to 280 meters. A larger search radius requires more processing time, due to more locations being evaluated to determine the best match for a ground control point (GCP).
If no value is specified for this parameter, the module uses the same value specified for the Search Radius parameter.
Chip Minimum Score
The threshold value that controls whether a candidate ground control point (GCP) is accepted or rejected as a GCP. This parameter specifies the minimum match quality that is considered an acceptable match, with 1.0 indicating a perfect match.
When using the Fast Fourier Transform Phase matching algorithm, this value is converted internally to a minimum acceptable phase shift peak value.
When using the Normalized Cross-Correlation matching algorithm, this value specifies the minimum match score value required to accept a local match between the input and reference images as a GCP. The system default value is 0.75.
GCP Priority
With this feature, you can set the priority of processing for one or more of your specific data aspects: your reference imagery, chip database, reference vectors, or GCPs you imported.
Each data aspect—reference imagery, chip database, reference vectors, or imported GCPs—has a GPC Priority parameter. The one with the highest selected value has the highest priority during processing. For example, if you set the GCP priority for your reference imagery to High, and the remaining aspects to Medium, the reference imagery is processed with the highest priority. Similarly, if you set the GCP priority for your reference imagery to Medium, and the remaining aspects to Low, the reference imagery is processed with the highest priority.
The Locked option retains the GCPs generated during the GCP collection process. GCP collection has two phases: collection and refinement. In the refinement phase, two separate subprocesses occur: GCP distribution and GCP refinement. During GCP distribution, the GCP priority applies. During the second phase, however, the GCP priority does not apply. Use this option when you want to retain the integrity of GCPs from a particular data aspect.
You can also set the GCP priority of more than one aspect to use the same value. That is, you can perhaps set a value of High to your reference imagery and your imported GCPs, as applicable.
Road Network
The path to a vector file that contains the road-network layers to use for automatic collection of ground control points (GCP).
GCP collection from the road network extracts GCPs by matching an optical image with rasterized lines.
The GCPs are collected automatically by matching patches in the image with roads. Matching is performed in the spatial-frequency domain using Fast Fourier Transform (FFT) to transform and match image patches and rasterized lines. The matching algorithm is based on the Kuglin and Hines (1975) paper cited in the References section later in this topic.
In a typical application, between 100 and 200 GCPs are extracted, with most of them being correct. The GCPs can then be used in automated image-orthorectification workflows. This method is well-suited to midresolution images from 5 meters through 15 meters.
Road Width Field Name
The field name of an attribute in the vector segment. The field must contain one or two numerical values. The values are converted to a line width, in meters. Float, double, and integer fields are supported.
If this parameter is not specified, then the following field names are checked:
- NBRLANES
- LANES
- ROADLANES
- ROADS
- ROADWIDTH
If none of these fields exist, then the Road Width Scale & Offset parameter defines the width of all lines in the file.
If the field does exist, and it contains a single value, then the scale factor part of the Road Width Scale & Offset parameter is used in the computation of the road width:
- road width (m) = fieldValue * roadWidthScale
If the field exists, and it contains two values, then the two values in the Road Width Scale & Offset parameter are used to compute the road width:
- road width (m) = fieldValue * roadWidthScale + roadWidthOffset
Road Width Scale & Offset
The scale and offset, in meters, used in conjunction with the Road Width Field Name parameter to compute the road width in meters. This parameter contains one or two values. The first value, the road width scale (roadWidthScale), must be greater than zero. If specified, the second value, the road width offset (roadWidthOffset), must be non-negative.
If no value is specified for the Road Width Field Name parameter, the road width scale defines the width of all lines (in meters) in the file and the second value is ignored.
- road width (m) = roadWidthScale
If a value for the Road Width Field Name parameter is specified, the scale and offset values are used to convert attribute values in the field to road width values, in meters. For more information, see the description of the Road Width Field Name parameter.
GCP Priority
With this feature, you can set the priority of processing for one or more of your specific data aspects: your reference imagery, chip database, reference vectors, or GCPs you imported.
Each data aspect—reference imagery, chip database, reference vectors, or imported GCPs—has a GPC Priority parameter. The one with the highest selected value has the highest priority during processing. For example, if you set the GCP priority for your reference imagery to High, and the remaining aspects to Medium, the reference imagery is processed with the highest priority. Similarly, if you set the GCP priority for your reference imagery to Medium, and the remaining aspects to Low, the reference imagery is processed with the highest priority.
The Locked option retains the GCPs generated during the GCP collection process. GCP collection has two phases: collection and refinement. In the refinement phase, two separate subprocesses occur: GCP distribution and GCP refinement. During GCP distribution, the GCP priority applies. During the second phase, however, the GCP priority does not apply. Use this option when you want to retain the integrity of GCPs from a particular data aspect.
You can also set the GCP priority of more than one aspect to use the same value. That is, you can perhaps set a value of High to your reference imagery and your imported GCPs, as applicable.
Polygon Vector File
The path to a file containing the polygon layers to be used for automatic collection of ground control points (GCP). The path may also specify a folder containing multiple polygon files.
GCP Priority
With this feature, you can set the priority of processing for one or more of your specific data aspects: your reference imagery, chip database, reference vectors, or GCPs you imported.
Each data aspect—reference imagery, chip database, reference vectors, or imported GCPs—has a GPC Priority parameter. The one with the highest selected value has the highest priority during processing. For example, if you set the GCP priority for your reference imagery to High, and the remaining aspects to Medium, the reference imagery is processed with the highest priority. Similarly, if you set the GCP priority for your reference imagery to Medium, and the remaining aspects to Low, the reference imagery is processed with the highest priority.
The Locked option retains the GCPs generated during the GCP collection process. GCP collection has two phases: collection and refinement. In the refinement phase, two separate subprocesses occur: GCP distribution and GCP refinement. During GCP distribution, the GCP priority applies. During the second phase, however, the GCP priority does not apply. Use this option when you want to retain the integrity of GCPs from a particular data aspect.
You can also set the GCP priority of more than one aspect to use the same value. That is, you can perhaps set a value of High to your reference imagery and your imported GCPs, as applicable.
GCP Text Files
The path and file name of a text file, or a folder of text files, that contains ground control points (GCPs) from other sources.
Each text file must have the MAPUNITS parameter specified in its required format. You can also specify up to two additional parameters, ELEVREF, and ELEVUNIT. The following table shows the supported values, a description, and an example for each parameter.
| Parameter | Supported values | Description | Example |
|---|---|---|---|
| MAPUNITS | PIXEL | Pixel and line | MAPUNITS=PIXEL |
| UTM | Universal Transverse Mercator | MAPUNITS=UTM 32 T D000 | |
| SPCS | State Plane Coordinate System | MAPUNITS=SPCS | |
| LONG/LAT | Longitude and latitude | MAPUNITS=LONG/LAT D000 | |
| METER | Image along-row and along-column, in meters | MAPUNITS=METER | |
| FEET | Image along-row and along-column, in feet | MAPUNITS=FEET | |
| ELEVREF | MSL | Mean sea level | ELEVREF=MSL |
| ELLIPS | Ellipsoid | ELEVREF=ELLIPS | |
| ELEVUNIT | METER | Meters | ELEVUNIT=METER |
| US_FEET | U.S. feet | ELEVUNIT=US_FEET | |
| FEET | Feet | ELEVUNIT=FEET |
When the path points to a folder, each text file must follow a naming convention, <filename>*GCP.txt, where <filename> is the file name of the ingested image.
- Name of the raw image file: image1_RAW_PAN.pix
- Name of the GCP text file: image1_RAW_PANGCP.txt
GCP Text File Format
The format of the file or files specified for the GCP Text File parameter, if specified.
The available formats, including a link to an example of each, are as follows.
| Format | Example |
|---|---|
| 2D: ID P L X Y S |
|
| 2DERR: ID P L X Y eP eL eX eY S |
|
| 3D: ID P L X Y Z S |
|
| 3DERR: ID P L X Y Z eP eL eX eY eZ S |
|
| Bulk GCP File |
|
GCP Priority
With this feature, you can set the priority of processing for one or more of your specific data aspects: your reference imagery, chip database, reference vectors, or GCPs you imported.
Each data aspect—reference imagery, chip database, reference vectors, or imported GCPs—has a GPC Priority parameter. The one with the highest selected value has the highest priority during processing. For example, if you set the GCP priority for your reference imagery to High, and the remaining aspects to Medium, the reference imagery is processed with the highest priority. Similarly, if you set the GCP priority for your reference imagery to Medium, and the remaining aspects to Low, the reference imagery is processed with the highest priority.
The Locked option retains the GCPs generated during the GCP collection process. GCP collection has two phases: collection and refinement. In the refinement phase, two separate subprocesses occur: GCP distribution and GCP refinement. During GCP distribution, the GCP priority applies. During the second phase, however, the GCP priority does not apply. Use this option when you want to retain the integrity of GCPs from a particular data aspect.
You can also set the GCP priority of more than one aspect to use the same value. That is, you can perhaps set a value of High to your reference imagery and your imported GCPs, as applicable.
Water Mask File
The path to a file that contains the polygon water-mask layer to be used for refinement of ground control points (GCPs). The path can also specify a folder that contains multiple water-mask files.
Refine GCPs
Selected by default, this check box controls whether to refine the ground control points (GCP). Refinement is to systematically eliminate GCPs that have large errors. To retain the integrity of the GCPs you have imported or otherwise referenced in a text file associated with the project, clear the Refine GCPs check box.
Rejection Method
The method used to reject ground control points (GCP).
- Automatic: selects the most appropriate rejection method, based on the sensor of the input scene
- RMS Error: RMS error-rejection method, measured in pixels
- Standard Deviation: sigma (standard deviation) rejection method, measured in pixels
- Percentage: percentage of points that can be rejected, starting with the one with the highest residual
- Absolute Distance: absolute distance rejection, in terms of the residuals, measured in pixels
- Absolute Number: absolute number of points to reject, starting with the ones with the highest residual
You can specify various values for this parameter, depending on the method selected.
Rejection Method Thresholds
The rejection threshold values for the value selected for the Rejection Method parameter.
-
Automatic: selects the most appropriate rejection method thresholds, based on the sensor of the input scene
-
RMS Error: rejection starts from the point with the largest residual error for any point, then recalculates the math model and RMS error. If the RMS error is still above the specified thresholds, the point with the next highest residual is removed and so on, until the x-RMS and y-RMS errors are equal to or less than THRESH1 pixels or THRESH2 pixels.
For example, a value of 2,2 rejects points with the highest residuals until the x-RMS and y-RMS are both less than two pixels.
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Standard Deviation: THRESH1 and THRESH2 represent the minimum standard deviation values of the x and y residuals to be rejected, respectively.
For example, a value of 2,1 rejects points that have a standard deviation higher than two of the resX mean, and rejects points that have a standard deviation higher than one of the resY mean.
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Percentage: THRESH1 represents the percentage of the number of points to be rejected and THRESH2 represents the ratio weighing between the x and y residuals. For example, if you set a rejection weight of 2, you are giving twice the weight to the x-residual (resX) as to the y-residual (resY). By default, the residual in x and y have the same weight. Therefore, if you have a point with a resX of 0.4 and a resY of 0.5, the point is given a resX of 0.8 and a resY of 0.5 for the rejection.
For example, a value of 5, 2 rejects five percent of GCPs, and gives the x-residual (resX) twice the weight as that of the y-residual (resY).
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Absolute Distance: THRESH1 and THRESH2 represent the minimum absolute x and y pixel residuals to be rejected. The rejection starts from the point with the largest x or y residual distance.
For example, a value of 2,2 rejects points with a resX of greater than two pixels, and rejects points with a resY of greater than two pixels.
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Absolute Number: THRESH1 represents the number of points to be rejected and THRESH2 represents the ratio weighing between the x and y residuals. For example, if you set a rejection weight of 2, you are giving twice the weight to the x-residual (resX) as to the y-residual (resY). By default, the residual in x and y have the same weight. Therefore, if you have a point with a resX of 0.4 and a resY of 0.5, the point is given a resX of 0.8 and a resY of 0.5 for the rejection.
For example, a value of 10, 0.5 rejects 10 GCPs, and gives the x-residual (resX) half the weight of the y-residual (resY).
Maximum GCPs
The maximum number of ground control points (GCPs) to accept.
After the module performs an initial collection of GCPs using the reference data, it refines the collection to ensure that only the most accurate points are retained.
If there are more GCPs collected than the specified maximum value, the module performs a second refinement, keeping only the GCPs with the lowest RMS error, up to the specified maximum number of GCPs. The second refinement uses the following rejection method:
RMS Error: for the second refinement, the rejection threshold value is set to the specified Maximum GCPs value.
Minimum GCPs
The minimum number of ground control points (GCPs) to accept.
After the module performs an initial collection of GCPs using the reference data, it refines the GCPs, and then verifies that the number of remaining GCPs is greater than or equal to the minimum number of GCPs.
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RMS Error: for the second refinement, both rejection threshold values (THRESH1, THRESH2) are quadrupled.
For example, an initial threshold of 1,1 is relaxed to 4,4.
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Standard Deviation: for the second refinement, both rejection threshold values (THRESH1, THRESH2) are quadrupled.
For example, an initial threshold of 2,1 is relaxed to 8,4.
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Percentage: for the second refinement, the first rejection threshold value (THRESH1) is divided in half; the second (THRESH2) is left as is.
For example, an initial threshold of 10,2 is modified to 5,2.
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Absolute Distance: for the second refinement, both rejection threshold values (THRESH1, THRESH2) are quadrupled.
For example, an initial threshold of 2,2 is relaxed to 8,8.
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Absolute Number for the second refinement, the first rejection threshold value (THRESH1) is doubled; the second (THRESH2) is left as is.
For example, an initial threshold of 20,0.5 is modified to 40,0.5.
If there are still too few GCPs after the second refinement, the module aborts and displays an error message.