Tie-Point Collection and Refinement module

Name	Caption
Input Scenes	Input scene folder
Output Folder	Output folder
Project Base Name	Base name for output files
Send Email	Email notification settings
Overwrite Results	Overwrite existing results
DEM Source	DEM tile source
Sampling Method	Tie-point-sampling method
TP Samples	Number of tie point samples to collect
Trials per TP	Number of attempts to collect a TP for each candidate
Distribution Area	Area of image in which to distrbute TPs
Matching Algorithm	Matching algorithm
Matching Channels	Matching channels
TP Search Radius	Tie point search radius
TP Minimum Score	Tie point minimum score
RF Math Model Order	RPC adjustment order
Thin Tie Points	Whether to thin tie points
Refine Tie Points	Whether to refine tie points
TP Rejection Method	Tie point rejection method
TP Rejection Method Thresholds	Tie point rejection method thresholds
Rejection Limit Percentage	Maximum percentage of TPs to eliminate per iteration
Rejection Action	Remove or deactivate TPs rejected during thinning and refinement

Parameter descriptions

Input Scenes

The path and name of a folder containing a valid CATALYST Professional OrthoEngine project file (oe.prj) or valid GDB-supported images.

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

Path and name of a folder of input images; for example, C:\data
Path and name of a folder containing an OrthoEngine project file; for example, C:\data\data-ingest_RAW_PAN.prj
Path and name of a folder with a wildcard to filter the imagery by file name or type; for example, C:\data\*_PAN.pix
Path and name of a folder path with a wildcard to filter by OrthoEngine project file; for example, C:\data\*_PAN.prj
You can also specify an MFILE as input. An MFILE is a text file with an .mfile file name extension. For more information on this type of file, see About the MFILE format.

Output Folder

The path and name of the folder to which to write the output files.

Project Base Name

The base name to use to prepend the names of the output files. That is, the value you specify is used as a prefix for each output file.

Specifying a base name for the output files helps you identify the files (and content thereof) according to the prefix.

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.

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.

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).

Sampling Method

The method to use to create tie-point samples from the source imagery.

Available options are:

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 and Grid 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 that it searches for in overlapping 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.
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.

Note: The tie-point-collection process may result in a higher or lower number of tie points than specified.

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).

You can select from the following:

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.

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, 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.

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.

RF Math Model Order

When using the Rational Function with satellite imagery, you can modify the RF math model to better agree with collected ground control points (GCPs) by selecting the correct RF math-model order.

Order 0: allows x,y translations
Order 1: allows x,y translations with rotation
Order 2: allows x,y translations with rotation and increased warping

Typically, performing a first-order transformation is best, except when the GCPs are not well distributed. If your GCPs are clustered together, a first-order transformation may introduce new and significant errors in the image away from the GCPs. If your GCPs are not well distributed, you will probably obtain better results with the zero-order transformation.

Thin Tie Points

Select this check box to thin the number of tie points (TP) collected.

That is, sometimes TP collection can gather too many points on each image. The distribution of the points may also be too varied. By thinning the points, you can reduce the number collected and distribute the points more consistently. Computation time is also reduced, because the detection of points requires less effort.

Refine Tie Points

Select this check box to refine the tie points collected. If this check box is left clear, you must verify and, if necessary, manually refine the tie points.

TP Rejection Method

The method used to reject tie points (TP).

Available options are:

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.

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

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:
1. RMS is less than 0.5 pixels
2. Improvements to the RMS are less than 0.05 pixels
3. 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).

Rejection Limit Percentage

The maximum percentage of the initial number of TPs to eliminate per refinement iteration.

Rejection Action

Select whether to remove or deactivate tie points (TP) rejected during thinning and refinement.

The available options are as follows:

Deactivate: renders rejected points inactive. Inactive points are excluded from the math-model calculation.
Delete: removes rejected points.

Details

General job details

Preprocessing requirements

Before running this module, the following requirements must be met to ensure the job processes successfully and produces accurate results:

Input satellite scenes: the imagery on which to collect tie points (TP) must contain a valid math-model segment. The segment can be the nominal one, as imported with the raw data during the ingest stage. In this case, the output folder of the Data Ingest module can be specified for the Input Scenes parameter. Alternatively, the input scenes can contain refined math models as a result of GCP collection. In this case, the output folder of the GCP Collection module can be specified for the Input Scenes parameter.

The overlapping images to be processed must have identical math-model types, including projection and datum. If input images have UTM projections, for a large area across UTM zones, it is recommended that you reproject the images to Long/Lat before running the module. It is also recommended that you process all imagery specified as input to this module with the Data Ingest and GCP Collection module.
Input aerial scenes: the folder specified for tie-point collection must contain a valid CATALYST Professional OrthoEngine project file. A valid project file contains all the aerial scenes, exterior orientation (EO), camera model, and map units. The input scenes can be the output folder of the Airphoto Ingest for AT module when it contains an OrthoEngine project file. Alternatively, the input scenes can be the output folder of the GCP Collection module.

Module details

The Tie-Point Collection and Refinement module reads the Input Scenes folder for valid imagery files or a valid OrthoEngine project file (oe.prj). Depending on the number of input scenes, the module creates one or more jobs to collect the TPs, each processing a subset of the input scenes. The module creates a separate OrthoEngine project for each subset (child job), which stores all collected TPs for the images in the given subset.

When all of the TP-collection jobs are complete, the module merges all the subset projects into a single one. The collected TPs are refined to meet the specified accuracy requirements, and a report summarizing the accuracy is created. A final OrthoEngine project file is created with the refined points and updated math model.

Note: When panchromatic data exists in the Input Scenes folder, TPs are collected only on the panchromatic data. The other scene types (MS, PSH, SAR) are copied to the output folder.

Job results

The Tie-Point Collection and Refinement module collects and refines the TPs of all input scenes. However, when the Input Scenes folder contains a data-ingest_RAW_PAN.prj or a gcp-collection_RAW_PAN.prj file, TPs are collected only on panchromatic scenes.

The module creates a CATALYST Professional OrthoEngine project file (oe.prj) for each scene type containing of all the refined and collected TPs and the refined math model. In addition, the Tie-Point Collection and Refinement module creates a series of summary files in text format.

The Tie-Point Collection and Refinement module produces the following output:

Final CATALYST Professional OrthoEngine project file of the collected and refined TPs.
Report, in text format, for the merged and final OrthoEngine project file.
Summary report, in text format, containing details of the processing.
Summary report, in text format, of the resulting accuracies of the images after TP collection and refinement.
A folder, intermediates, which contains files of intermediate results. You can use these files to rerun the job, if necessary. After completing quality assurance (QA), you can remove this folder.

You can now open the scenes in CATALYST Professional Focus or open the OrthoEngine project file in OrthoEngine to perform QA, or proceed to use the next module in the CATALYST Enterprise workflow. Typically, this is to use one or both of the following:

DEM Extraction module
Orthorectification module

Tie-Point Collection and Refinement module

Description

Parameters

Parameter descriptions

Details