Tie-Point Collection and Refinement ADS module

Name	Caption
Input Scenes	Input OrthoEngine project file or PCIDSK files
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
Matching Algorithm	Matching algorithm
Matching Channels	Matching channels
TP Search Radius	Tie point search radius
TP Minimum Score	Tie point minimum score
Same as Nadir to Nadir	Whether to apply nadir-to-nadir TP-collection parameters
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 file name of the OrthoEngine project file (*.prj) in which you want to collect and refine TPs. Alternatively, you can enter the path and file name, and using a wildcard, such as the asterisk, of one or more files in PCIDSK (.pix) format.

For example, you can specify the parameter by using the following:

Path and name of a folder containing an OrthoEngine project file; for example, C:\data\<file_name>.prj
Path and name of a folder with a wildcard to filter the imagery by file name or type; for example, C:\data\*.pix

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 file or folder containing DEM tiles.

This value may be the name of a single DEM file, the name of a folder containing DEM tiles, or the name of an index.txt file.

If the name of an existing folder is specified, the module reads the folder for a file named index.txt, and a set of DEM raster tiles. The index.txt file lists the DEM files contained in the specified folder and provides information describing each raster tile.

For more information on the format of the index.txt file and specific requirements for the individual DEM tiles, see Format of the DEM index file.

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.

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.
Sharpened Fast Fourier Transform Phase: when two images have a relative shift between them, the result is a phase difference in the frequency domain. Sharpened Fast Fourier Transform Phase (SFFTP) determines the shift between images using the phase difference. A sharpening phase follows, which attempts to match each point with higher confidence. Some points may be rejected if sharpening finds little correlation between matches.
Sharpened Fast Fourier Transform Phase - No Reject: When two images have a relative shift between them, the result is a phase difference in the frequency domain. Sharpened Fast Fourier Transform Phase - No Reject (SFFTPNR) determines the shift between images using the phase difference. A sharpening phase follows, which attempts to match each point with higher confidence. Points are not rejected if sharpening finds little correlation between matches.
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, SFFTP, or SFFTPNR. Typically, NCC also generates faster results, because the template size it uses is smaller than that used by FFTP, SFFTP, or SFFTPNR.

For more consistently accurate results, SFFTP 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 SFFTP 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. SFFTP 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.

Same as Nadir to Nadir

Select this check box to apply the same TP-collection parameters as in the nadir-to-nadir pass.

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:

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:

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:

You must have a minimum of two or more overlapping ADS images
The imagery on which to collect tie points (TP) must contain a valid ADS math-model segment. The segment can be the nominal one imported with the raw data during the ADS-ingest stage. In this case, the output folder of the ADS Ingest module can be specified as the value of Input Scenes. Alternatively, the input scenes can contain refined math models produced by GCP collection. In this case, you can specify the output folder of the GCP Collection module as the value of Input Scenes.
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 this module.

Module details

The Tie-Point Collection and Refinement ADS module reads the Input Scenes folder for valid ADS imagery files or a valid CATALYST Professional OrthoEngine project file (.prj). Depending on the number of input scenes, the module creates one or more jobs to collect the TPs, each processing a single input scene. The module collects TPs in two passes. The first collects TPs for all the nadir images matching nadir-to-nadir images. The second pass uses the TPs collected during the first pass as candidates and collects TPs between the nadir-and-forward images, and the nadir-and-backward images.

When all of the TP-collection jobs are complete, the module merges all the individuals projects into 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: TPs are collected only on the active images when the value of Input Scenes is an OrthoEngine project file.

Job results

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

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

Final 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.
An intermediates folder, 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.

Each output file is prefixed with the value specified for Project Base Name.

After processing is complete you can open the scenes in CATALYST Professional Focus, 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 run one or both of the following:

DEM Extraction module
Orthorectification module

Tie-Point Collection and Refinement ADS module

Description

Parameters

Parameter descriptions

Details