Image Coregistration module

Name	Caption
Input Folder	Input folder
Output Folder	Output folder
Output File Type	Output file type
Output File Options	Output file options
Overwrite Results	Overwrite existing results
Send Email	Email notification settings
Reference Images	Input reference images folder
Reference Channel for Matching	Reference image channel to use for matching
DEM Source	DEM tile source
Pair Selection Method	Image pairing method
Master Matching Channel	Master matching channel
Math Model	Input math model
RPC or Polynomial Math Model	RPC or polynomial math model
Sampling Method	GCP sampling method
GCP Samples	Number of GCP samples to collect
Matching Algorithm	Matching algorithm
Collection Strategy	Number of passes for GCP collection
Search Radius	Search radius (pixels)
Minimum Score	Minimum score (percentage)
Refine GCPs	Whether to refine GCPs
Rejection Method	GCP rejection method
Rejection Method Thresholds	GCP rejection method thresholds
Water Mask File	Input water-mask file
Output Map Units	Output projection
Output Pixel Size	Output pixel size
Resampling Method	Resampling method
Output Product Type	Output product type
Transfer RPC from PAN to MS	Transfer RPC from PAN to MS

Parameter descriptions

Input Folder

The path and name of the folder of images to coregister.

The input folder can contain raw or orthorectified images (created with the Orthorectification module. If necessary, you can use a wildcard, such as the asterisk (*), to filter the input images.

This parameter is mandatory.

Output Folder

The path and name of the folder to which to write the output coregistered images. This folder must be one other than that specified for the Input Folder parameter.

The names of the output files are created according to the value selected for the Output Product Type parameter. For example, when you select Raw (with math model), the names of the source files are used for the names of the output images. When you select Orthos , the names of the output files are appended with _ORTHO.

This parameter is mandatory.

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.

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.

Note: This module uses all of the images in the input folder. Ensure that the specified folder contains only the images destined for use as reference for automatic GCP collection. For example, in some cases, the mosaic tile folder contains a mosaic preview file and scaled tiles; because these are not appropriate for automatic GCP collection, ensure that there are no such files in the folder specified for Reference Images.

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.

DEM Source

The name of a single digital elevation model (DEM) file or a folder containing one or more DEM tiles.

This parameter can be specified by using any of the following:

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).

Note: With some modules, the DEM Source parameter is mandatory. That is, if in the interface an asterisk appears beside the parameter label, it is mandatory and you must enter a value; otherwise, the parameter is optional.

Pair Selection Method

Select whether to use the input image or a reference image for image-pair selection.

Available options are:

MS and PAN Pair: (default) selects a pair consisting of one multispectral input image and one or more panchromatic references images, if overlapped.
Left and Right Camera Pair: selects a left and right camera image pair. This method is used typically with CBERS-02C HRC images.
Spatial Overlap: selects the input image with overlapping reference images as a pair, matching the images based on spatial overlap.

This parameter is mandatory.

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.

Available options are:

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.

Available options are:

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 and Susan options determine how to find the initial candidate positions in one image (the source image) for collecting sample points. The function builds a patch around each candidate position, and searches within that area for corresponding features in the overlapping images.

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.

Available options are:

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.

The available options are as follows:

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.

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.

Note: If under Matching in the Math Model list you selected File Georeferencing Only, a polynomial math model will be generated and the GCPs will not be refined; that is, this parameter is ignored.

Rejection Method

The method used to reject ground control points (GCP).

Available options are:

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.

This parameter is defined using two values (THRESH1, THRESH2); these differ in meaning depending on the selected rejection method:

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).

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.

Output Map Units

The projection of the output imagery.

The value of this parameter must be in the PCI Projection String format.

The standard definitions are:

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).
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
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.

Output Pixel Size

The sample size of the output imagery.

The output pixel size must be specified in the value (units) of Output Map Units; for example, when the value of Output Map Units is specified as a UTM zone, the pixel output size must be in meters. When the value is specified as Long/Lat, the pixel size must be in decimal degrees.

If a single value is specified, that value applies to both x and y values.

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 folder.

Resampling Method

The resampling method to use during processing.

Available resampling options are:

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.
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.

Output Product Type

The type of output product to create. This parameter applies only to input images in PCI raw format.

Available options are:

Orthos: creates orthorectified coregistered output images
Raw (with math model): creates raw coregistered output images with a math model; after coregistration, no orthorectification occurs

This parameter is optional.

Transfer RPC from PAN to MS

Select this check box to transfer the input RPC math model from panchromatic to multispectral.

This option is applicable only when MS and PAN Pair is selected for the Pair Selection Method parameter and a rational function model exists in both the MS and PAN images.

Image Coregistration module

Description

Parameters

Parameter descriptions

Details