Parameter descriptions
MS Scene Folder
The path and name of the folder that contains multispectral imagery to pansharpen.
- Raw data
- Orthorectified using the Orthorectification module
- Previously imported to PCIDSK using the Data Ingest module
You can use a wildcard, such as the asterisk (*), to filter input images. This is supported only for orthorectified or ingested data; raw images cannot be filtered.
This parameter is mandatory.
PAN Scene Folder
The path and name of the folder that contains panchromatic data to pansharpen.
If no value is specified for this parameter, the module reads the folder specified for the MS Scene Folder parameter for suitable data.
You can use a wildcard, such as the asterisk (*), to filter input images. This is supported only for orthorectified or ingested data; raw images cannot be filtered.
This parameter is mandatory.
Output Folder
The path and name of the folder to which to write the output files.
Output File Type
The format of the output file.
For more information on the supported file formats, see GDB-supported file formats.
Output File Options
The options to apply when creating the output file or files. The available options are specific to the file format; in each case, the default of no options is allowed.
For more information on the options available for the output file type you specify, see GDB-supported file formats.
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.
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.
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).
Import Data
Selected by default, this check box controls whether to ingest the input data and convert it to PCIDSK format.
When clear, the module first checks if an ingested copy of the input scenes exists already. If so, those scenes are used as input for collection of ground control points (GCP). If a copy of the ingested scenes is not found, the scenes in the specified scene folder are used as input for GCP collection. In the latter case, however, the scenes must be in a GDB-compatible format.
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.
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.
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.
Band Coregistration
Select this check box to geometrically coregister bands in one multispectral scene.
When you select this check box, you must select an appropriate option from the Band Coregistration Order list.
Band Coregistration Order
From the list, select the order in which you want band coregistration to occur during processing.
- Before GCP Collection: this is the default option
- After GCP Collection
Reference Channel
The channel to use as reference to coregister the other bands.
If no value is specified for this parameter, the module uses the channel number set in the B2B Master Band metadata tag, if it exists; otherwise, it uses the first channel.
Maximum Shift Between Bands
The estimated maximum shift between bands, in pixels. If no value is specified for this parameter, the module uses the value set in the B2B Misregistration Shift metadata tag, if it exists; otherwise, the value defaults to 20 pixels.
This estimate helps in the search for ground control points (GCPs) between bands for coregistration.
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.
Math Model
The math model to use for collection of ground control points (GCPs) and, subsequently, orthorectification.
- Rational Function Model (RPC): uses the Rational Polynomial Coefficients (RPC) 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. This is the default value.
- 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.
- File Georeferencing Only: available with select CATALYST Enterprise modules, this option uses the file-level georeferencing information, such as the projection and the extents, to match the multispectral data to the panchromatic data. That is, if no math model exists in the input data, this option is applied regardless of which option is selected for this parameter.
The module first attempts to use the selected math model. If required information is missing from the specified math model, the module automatically tries to use another math-model option and, subsequently, a warning message is displayed.
RPC or Polynomial Math Model
The order of the Rational Function Model (RPC) or polynomial math model.
Compute RPC from GCPs
Allows you to select whether to force the calculation of the Rational Function Model (RPC) models from ground control points (GCPs).
This option allows processing of HJ-1 scenes.
- No
- Yes
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, NCC also generates faster results, because the template size it 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.
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.
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.
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 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 |
|
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.
-
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.
-
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).
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
Collect Tie Points
Select this check box to collect tie points (TP) on valid overlapping scenes.
Sampling Method
The method to use to create tie-point samples from the source imagery.
- 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; this is the default value
- 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.
- The Grid option creates candidates in a grid-like pattern. This option tends to be faster, as it does not preprocess the image. 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.
For tie-point collection, the Susan option is often preferred because it facilitates performing quality assurance on the collected tie points: you can visualize a recognizable feature (perhaps a building corner or specific tree, for example) and compare it with the other image to see if it matches correctly.
TP Samples
The maximum number of tie points to collect.
When using the Grid option, acceptable 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 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 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.
When 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.
Trials per TP
The maximum number of attempts to find a match. The module attempts to match the primary sample point and, if that fails, selects alternate points within the same grid cell until a match succeeds or the number of trials is reached. The value you specify must be an integer ranging from 1 through 500.
Distribution Area
The area of each image in which to distribute tie points (TP).
- Entire: For use typically with aerial imagery, evenly distributes TPs in the entire image being matched. The number of samples is multiplied by the overlap percentage and the density of candidate distribution is relatively constant.
- Overlap: For use typically with satellite imagery in which the overlapping areas may be less regular than aerial imagery, evenly distributes TPs only in the area that overlaps the adjacent image. Smaller overlaps provide a distribution of candidates that is more dense.
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.
Matching Channels
The channel or channels in the image file from which to extract the TPs.
When this parameter specifies multiple channels, the channel data is averaged together. If no value is specified for this parameter, its value defaults to channel 1.
TP Search Radius
The maximum search radius, in pixels, for TPs. This controls the size of the area to search when seeking a match. A higher value increases the search area to be considered while matching each point, thereby increasing the processing time. Pixels in the search area is inspected for similarity within a small window (template) from the raw image. The pixel with the highest degree of similarity is accepted if it passes the match-acceptance criteria. The specified value should be slightly greater than the expected inaccuracy of registration between the two images, due to all possible causes. For example, if the nominal model of a satellite image is known to be accurate to approximately 100 pixels, and DEM inaccuracies can add another 30 pixels, then the search radius should be set to about 150 pixels. The poorer the accuracy of the initial match location estimate, the larger the search radius should be.
TP Minimum Score
The minimum-acceptance value for the correlation score that is considered to be a valid match. Valid values range from 0 to 1. For a match to become a TP, its match score must be greater than the specified value.
TP Rejection Method
The method used to reject tie points (TP).
- Automatic: RMS error and sigma (standard deviation) rejection methods, measured in pixels
- RMS Error (Pixel): RMS error-rejection method, measured in pixels
- Standard Deviation (Pixel): 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 (Pixel): 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.
TP Rejection Method Thresholds
The rejection threshold values for the value selected for the Rejection Method parameter.
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Automatic: rejection starts by removing the top 5 percent of TPs with the largest residual values that are greater than plus-minus three (±3) standard deviations, and then recalculates the math model. This is performed three times when the number of TPs is less than 100,000, and performed only once when the number of TPs is equal to or greater than 100,000. The rejection method then removes all TPs that are plus-minus three (±3) standard deviations and recalculates the math model. The refinement procedure ends when:
- RMS is less than 0.5 pixels
- Improvements to the RMS are less than 0.05 pixels
- Maximum number of iterations has been reached
The maximum number of iterations is 10 when the number of TPs is less than 100,000, and five when the number of TPs is equal to or greater than 100,000.
-
RMS Error: rejection starts from the point with the largest residual error for any point, and 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.
-
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.
-
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 TPs, and gives the x-residual (resX) twice the weight as that of the y-residual (resY).
-
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.
-
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 TPs, and gives the x-residual (resX) half the weight of the y-residual (resY).
Orthorectify
Select this check box to orthorectify the input scenes.
With this check box selected, the module orthorectifies the images in the order specified for the Orthorectification Order parameter.
Orthorectification Order
The order in which to perform the orthorectification.
- After pansharpening: orthorectification occurs after images have been pansharpened
- Before pansharpening: orthorectifcation occurs before beginning the pansharpening process
Output Map Units
The projection of the output imagery.
The value of this parameter must be in the PCI Projection String format.
- PIXEL: Pixel and line (not for use with DEM extraction)
-
UTM: Universal Transverse Mercator
The value specified can be the UTM grid zone number and row, and Earth model, as follows:
UTM [mm] [r] [Ennn]
where:- [mm] is the two-digit zone number between 1 and 60
- [r] is the zone row: a single letter between N and X for north of the equator and between C and M for south of the equator. Zone rows A, B, Y, and Z are not supported; rows I and O do not exist.
- [Ennn] specifies the Earth model, where the model number is between 0 and 19. If the Earth model is not specified, it is assumed to be E000 (Clarke 1866).
-
SPCS: State Plane Coordinate System
The SPCS zone number and Earth model can be specified as follows:
SPCS [mmmm] [Ennn]
where:- [mmmm] is the four-digit zone number
- [Ennn] specifies the Earth model, where the model number is between 0 and 19. If the Earth model is not specified, it is assumed to be E000 (Clarke 1866).
-
LONG/LAT: Longitude and latitude
The Earth model can be specified for LONG/LAT (and other units except PIXEL), as follows:
LONG/LAT [Ennn]
If the Earth model is not specified, it is assumed to be E000 (Clarke 1866).
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EPSG: European Petroleum Survey Group code
You can specify the projection by entering an EPSG code defined by the Open Geospatial Consortium (OGC). For information on the code definitions, visit epsg.org and spatialreference.org.
The EPSG code is specified using the EPSG keyword followed by an integer and separated by a colon; for example:
EPSG:4326
Most common EPSG codes are supported.
-
METER: Image along-row and along-column meters
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FEET: Image along-row and along-column feet
-
You can also specify the label of a projection you define, if the projection exists in the userproj.txt file; otherwise, you must enter the projection-parameter information as a string separated by the vertical bar (|). The projection-parameter string defines 18 parameters delimited by spaces, including the following:
- Dearth
- RefLong
- RefLat
- StdParallel1
- StdParallel2
- FalseEasting
- FalseNorthing
- Scale
- Height
- Long1
- Lat1
- Long2
- Lat2
- Azimuth
- LandsatNum
- LandsatPath
- UnitsCode
For example, the projection named 'France93' can be specified, as follows:LCC D350 | 0 0 3.0 46.5 44.0 49.0 700000 6600000 0 0 0 0 0 0 0 0 0 -1
If you do not specify a value for Output Map Units, the map unit of the input image is used for the output image. If the input data is a variety of map units, the map unit of each output image is that of its corresponding input image. In such a case, it is recommended that you specify the output map units.
You can also specify the label of a projection defined in the userproj.txt file.
Panchromatic Pixel Output Size
The output spatial resolution for the panchromatic imagery to be orthorectified.
The units for the pixel size must match the units selected for the Map Units parameter; for example, if the map units are specified as UTM, the panchromatic pixel output size is in meters.
If no value is specified for this parameter, the pixel output size is based on the input math model associated with each scene in the input-scenes folder.
Multispectral Pixel Output Size
The output spatial resolution for the multispectral imagery to be orthorectified.
The units for the pixel size must match the units specified for the Map Units parameter; for example, if the value of Map Units is specified as UTM, the multispectral pixel output size is in meters.
If no value for this parameter is specified, the pixel output size is based on the input math model associated with each scene in the input-scenes folder.
Resampling Method
The resampling method to use during processing.
- Cubic: cubic convolution. This method determines the gray level from the weighted average of the 16 pixels closest to the specified input coordinates and assigns that value to the output coordinates. The resulting image is slightly sharper than one produced by bilinear interpolation, and it does not have the disjointed appearance produced by nearest-neighbor interpolation. This is the default value.
- Nearest Neighbor: nearest-neighbor interpolation. This method identifies the gray level of the pixel closest to the specified input coordinates and assigns that value to the output coordinates. Although this method is the most efficient in computation time, it introduces small offsets in the output image. The output image may be offset spatially by up to half a pixel, which may cause the image to have a jagged appearance.
- Bilinear: bilinear interpolation. This method determines the gray level from the weighted average of the four closest pixels to the specified input coordinates and assigns that value to the output coordinates. An image with a smoother appearance than nearest-neighbor interpolation is created, but the gray-level values are altered in the process, resulting in blurring or degraded image resolution.
- Lagrange-4: four-point Lagrange interpolation
- Lagrange-8: eight-point Lagrange interpolation
- Sinc-8: eight-point sin(x)/x
- Sinc-16: 16-point sin(x)/x
- Average: Average resampler for creating downsampled products
- Gaussian: Gaussian resampler
- Median: Median resampler for creating downsampled products
Resampling Method Extra Options
- Sin(x)/x with an 8 x 8 window
- Sin(x)/x with a 16 x 16 window
SHAPINGWINDOW=[sw],BETA=[beta]
where:
SHAPINGWINDOW specifies a window to attenuate the SINC coefficients to reduce resampling artifacts. The value can be KAISER, HAMMING, HANN, LANCZOS, PARABOLA, or NONE. SHAPINGWINDOW is optional; the default value is KAISER. BETA is applicable only when SHAPINGWINDOW is KAISER. SHAPINGWINDOW determines the shape of the KAISER window; a larger BETA value produces greater attenuation of the SINC coefficients. Its value can be between 1.0 and 10.0. BETA is optional.
- Average
NUMCOLS=[nc],NUMROWS=[nr]
- Median
NUMCOLS=[nc],NUMROWS=[nr]
where:
NUMCOLS and NUMROWS can be any value between 1 and 11.
- Gaussian
DSFACTORCOL=[dc],DSFACTORROW=[dr]
where:
DSFACTORCOL is the fraction downsampling factor in col (>=1). If not specified, a default factor is computed automatically based on the output and input pixel sizes. DSFACTORROW is the fraction downsampling factor in row (>=1). If not specified, default to the value of DSFACTORCOL.
MS and PAN Coregister
Specifies whether to coregister the input scenes.
If selected, the module geometrically coregister the input images. The order in which coregistration is applied is specified by the MS and PAN Coregistration Order parameter.
If cleared, images are not coregistered.
MS and PAN Coregistration Method
The method to use for MS and PAN coregistration.
- Super Registration: use super registration to perform MS and PAN alignment
- Polynomial Approach: use polynomial approach to perform MS and PAN alignment
MS and PAN Coregistration Order
The order in which images are coregistered.
- After orthorectification: image coregistration occurs after images have been orthorectified
- Before pansharpening: image coregistration occurs before beginning the orthorectification process
Grid Spacing
The spacing in pixels between the points on a grid, for matching on the reference image.
Values between 1 and 500 are supported and typically a value between 10 and 50 is used. Smaller grid spacings can model finer mismatch detail and take longer to run. In most cases a value of 25 is a good balance between detail and running time. If the orthorectified imagery is far apart in time (for example 5 years) and looks quite different, a smaller grid may be necessary since many of the grid locations may fail to find a match.
If you do not specify a value for this parameter, 25 is used, by default.
FFT Size
Specifies the FFT template matching size in pixels. Larger sizes tend to have fewer blunders, but are less precise and run more slowly.
Generate Offsets For Every Pixel
Select this check box to to generate X/Y offsets all valid pixels.
Generating an offset for every pixel produces better overall results, at the cost of more processing time and increased disk space.
Point Matching Strategy
The strategy to be used for managing matching points when multiple reference images are provided.
- First: This strategy is fastest by choosing the matching point from the first reference processed.
- Best: This strategy selects the highest quality matching points from all available reference images.
Point Cleaning Level
Level of filtering, to eliminate bad matches, that is applied to the match points. Filtering removes blunders and smooths the results. The more filtering applied, the more blunders are removed and smoothing is done, resulting in potentially less accurate results.
- Low: apply only a small amount of filtering. Use this option sparingly. Only use it if the input images and reference images are taken at nearly the same time and are extremely similar. Eliminating most of filtering for such images can improve performance with very little degradation in quality.
- Medium: apply only a medium amount of filtering. This option should almost always be used.
- High: apply a high amount of filtering. This option should be used when the input images and the reference images might have large differences.
- None: do not apply any filtering. This option should almost never be used. Only use it if the input images and reference images are taken at nearly the same time and are extremely similar. Eliminating filtering for such images can improve performance with very little degradation in quality.
Exclusion Masks To Use
Whether to exclude match points due to the bitmap exclusion masks in the reference image or input image.
- In Both Input and Reference Image: use the bitmap exclusion masks in both the input images and the reference images.
- In Reference Image Only: only use the exclusion mask in the reference image.
- In Input Image Only: only use the exclusion mask in the input images.
- None: do not use any exclusion masks.
Math Model
The math model to use for collection of ground control points (GCPs) and, subsequently, orthorectification.
- Rational Function Model (RPC): uses the Rational Polynomial Coefficients (RPC) 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. This is the default value.
- 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.
- File Georeferencing Only: available with select CATALYST Enterprise modules, this option uses the file-level georeferencing information, such as the projection and the extents, to match the multispectral data to the panchromatic data. That is, if no math model exists in the input data, this option is applied regardless of which option is selected for this parameter.
The module first attempts to use the selected math model. If required information is missing from the specified math model, the module automatically tries to use another math-model option and, subsequently, a warning message is displayed.
RPC or Polynomial Math Model
The order of the Rational Function Model (RPC) or polynomial math model.
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.
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.
-
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.
-
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).
-
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.
-
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).
Transfer RPC from PAN to MS
Select this check box to transfer the RPC model from panchromatic to multispectral.
This parameter applies only if a PAN and MS pair is selected for image coregistration and a rational math model exists in both MS and PAN files.
Pansharpening Method
The method to use for pansharpening.- MRA Fusion: creates high-resolution color images by fusing black-and-white panchromatic and multispectral color images by using wavelet decomposition.
- UNB Pansharp: developed by the University of New Brunswick, this method creates high-resolution color images by fusing black-and-white panchromatic and multispectral color images. The method uses an algorithm that produces sharpening results superior to other types while preserving the spectral characteristics of the original images.
Multispectral Sharpening Channels
A comma-delimited list of the multispectral channels to sharpen.
If no value is specified for this parameter, all channels are processed by default. These channels are fused with the high-resolution, panchromatic image data.
Multispectral Reference Channels
A comma-delimited list of the multispectral channels to use as reference for the sharpening process.
If no value is specified for this parameter, all channels are processed by default. These channels, and those of the panchromatic image, span the same range of frequency (wavelength) response.
When no reference channel is specified, the module determines the appropriate reference bands based on the available wavelength information for both the panchromatic and multispectral files.
Resampling Method
The resampling method to use during processing.
- Cubic: cubic convolution. This method determines the gray level from the weighted average of the 16 pixels closest to the specified input coordinates and assigns that value to the output coordinates. The resulting image is slightly sharper than one produced by bilinear interpolation.
- Bilinear: bilinear interpolation. This method determines the gray level from the weighted average of the four closest pixels to the specified input coordinates and assigns that value to the output coordinates. An image with a smoother appearance than nearest-neighbor interpolation is created, but the gray-level values are altered in the process, resulting in blurring or degraded image resolution.
The resampling method is only available for UNB pansharpening.
Enhance
Specifies whether to generate a refined pansharpened image.
The enhance option is only available for UNB pansharpening.
Edge Sharpen
Amount of sharpening to image edges to apply. A value of 1.0 (default) means no edge sharpening. Values greater than 1.0 apply greater sharpening. Values greater than 2.0 are not recommended.
Applying edge sharpening makes image more visually appealing but may degrade radiometric similarity with the original multispectral (MS) data.
AdaptRadiometry
Method to adapt the output spectral values to those of the input multispectral (MS) image.
Adapting the radiometry can better match to the original multispectral image. The two methods are linear regression based on a small sliding window and an estimate of the Modulation Transfer Function (MTF).
- YES: applies both the MTF and LIN options (default)
- ALL: applies both the MTF and LIN options (default)
- MTF: applies MTF
- LIN: applies Linear
- NO: no adapting radiometery applied