Mosaic from Raw Scenes module

• Path and name of a folder of input images; for example, C:\data.
• Path and name of a folder with a wildcard to filter the imagery by file name or type; for example, C:\data\*.xml.
• 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.

• 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.

• Multispectral (MS)
• Panchromatic (PAN)
• Pansharpened (PSH)
• MS & PAN (for Pansharpening)

• 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.

• 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).

• All
• Any

• 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.

• 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

• 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.

• 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.

• 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.

• Fast Fourier Transform Phase
• Normalized Cross-Correlation

• Name of the raw image file: image1_RAW_PAN.pix
• Name of the GCP text file: image1_RAW_PANGCP.txt

• 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

• 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).

• 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.

• 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.

• 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.

• 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.

• 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

• 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).

• After orthorectification (default)
• Before orthorectification

• 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.

• 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

• 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

• 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

• 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

• 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.

• None: The source images are not resorted; rather, they are processed in the order provided.
• Nearest to Center: If an image is specified as a value for Starting Image, that image is processed first. If no starting image is specified, the image most central of the input scenes is processed first. Subsequent images are processed in turn, based on which has its center closest to the center of the first image. All calculations (determining the center point) are based on the rectangular bounding box of each image.
• Maximum Intersection: If an image is specified as a value for Starting Image, that image is processed first. If no starting image is specified, the image most central of all the input scenes is processed first. Subsequent images are processed in turn, based on which image most intersects (overlaps or covers) the images already processed. All calculations, such as determining overlap amounts, are based simply on the rectangular extents bounding box of each image.

• Hot Spot: Removes any hot spots from the input images. A hot spot is a common distortion in aerial photographs and optical satellite images. This distortion is typically the result of solar reflections that appear circular in photographs, and tends to appear as a striped pattern in optical satellite images.

  When Hot Spot is selected as the normalization method, hot spots are removed from the images by normalizing the brightness over the image by fitting a Gaussian surface to the brightness values. It does not remove the smaller spot reflections from lakes, cars, or buildings.

• Adaptive: Balances the brightness and contrast using a moving window. The adaptive filter is recommended for images with a large, irregular bright and dark pattern that cannot be modeled to a Gaussian surface. Patterns that model to a Gaussian surface are better handled by Hot Spot.

  The adaptive filter adjusts the brightness and contrast over local areas, thereby improving image detail, while reducing the bright-and-dark pattern over the entire image. It applies an adaptive enhancement using a moving window to calculate the adjustment for each pixel value. The filter calculates the mean and standard deviation of the gray levels within the window and adjusts the values to match the overall mean and standard deviation of the image. The mean is used to adjust the brightness and the standard deviation is used to adjust the contrast.

  Note: Because of the intensive background processing required, Adaptive normalization can be slow to produce results.
• None: Applies no normalization to the input images.

• Bundle: Typically produces the best-looking color-balancing results using two complimentary phases. First, a global adjustment of the mean and sigma of each image is computed using a "block-bundle" method between it and each of its overlapping images. This global adjustment produces large adjustments between images to make them more balanced with each other. Second, a series of dodging points is determined automatically to make smaller local adjustments between pairs of images. This technique ignores the order of the input images, because all of the overlapping images are used to adjust one image. Bundle is recommended for most images.
• Overlap: Performs a least-squares analysis in the overlap areas between images in the mosaic to determine the optimal radiometry for the final mosaic. When the mosaic is created, the computed transformation is then applied to each image as it is added to the mosaic.
• LUT: Uses previously stored lookup-table segments from the source images to control the color balancing. The module seeks out a LUT segment for each image channel. When this option is selected, the LUT-segment numbers must be specified for Color Balancing Specification Extra Options.
• Histogram: Performs color balancing based on matching the input image histograms to the target mosaic image, sequentially. This method does not require very precise georeferencing and overlap.
• Reference: Color balancing is based on matching source-image histograms with those of the specified reference images. When this option is selected, the reference image must be specified for Color Balancing Specification Extra Options.
• Neighborhood: Determines a set of color-balancing coefficients that change each image pixel based on the pixels values of the intersecting (neighboring) images, regardless of z-order. The overall pixel value of each individual image is adjusted iteratively, so that the value is similar to those of its neighbor images. Overlap regions between the image and its neighboring images are used to solve the equations of a multifactor model, which represents the pixel values of the image.
• None: Images are added to the mosaic exactly as they are and the pixel values are not be further adjusted.

• Bundle: <Autoscale>

  Autoscale can be specified for all input images to be stretched linearly and have a common scale. This can improve the results when different input images have significantly different dynamic ranges.

• LUT: <LUT_segment_number>

  LUT_segment_numbers explicitly specify the LUT-segment numbers to use for color balancing. The first LUT is used for the first image channel, the second LUT for the second channel, and so on. All input images must have the specified LUT segments; otherwise processing of the job stops and a message detailing the event appears.

• Histogram: [<match_area_multiple>], [<trim_percentage>]

  • match_area_multiple: Used to determine the amount of data in the mosaic file used to compute the reference histogram. If 1 is specified, the reference histogram is computed based on an area equal to the overlap between the incoming image and the output mosaic. If 3 is specified, the reference histogram is computed based on an area equal to three times the overlap between the incoming image and the output mosaic
  • trim_percentage: A number representing the percentage removed from the upper and lower parts of the histogram range. Trimming is to ignore the specified trim percentage of the total histogram area from the ends of both tails in a gray-value histogram when matching with another target histogram. Trimming is applied on both the input image and the target image. The default value is 2 percent.

• Reference: <fili_ref>

  fili_ref specifies the reference image file

• Neighborhood: [<meanbias_adjustment>], [<outlier_correction>], [<outlier_threshold>]

  • meanbias_adjustment: Set to 3 by default, adjusts the overall tonal value of the image pixels in each overlapping region, to minimize the outliers
  • outlier_correction: Used to determine if extreme pixel values considered as outliers should be excluded from the computation of the coefficients. The default is set to YES, meaning that an outlier correction is applied.
  • outlier_threshold: Used only if outlier_correction is set to YES; it is set to 1 by default, meaning that pixel values that fall beyond the mean-difference value + 1 standard deviation are excluded from the computation of the coefficients.

• Last Vector: Sets the last-created vector segment in file specified for Global Color Balance Mask File as the global color-balance mask layer. The vector segment must contain a polygon, and all image pixels enclosed within it is excluded from the color balancing calculation.
• Last Bitmap: Sets the last-created bitmap segment in the file specified for Global Color Balance Mask File as the global color-balance mask layer. All image pixels covered by the bitmap is excluded from the color-balancing calculation.
• Specific Segment: Defines a specific segment in the file specified for Global Color Balance Mask File as the global color-balance mask layer. All image pixels covered by the mask area is excluded from the color-balancing calculation. When this option is selected, you must specify a value for Global Color Balance Mask Segment.

• Minimum Squared Difference: Specifies the minimum-squared-difference method. This method is suitable for most mosaicking projects, and in most cases produces the cleanest cutlines. The algorithm determines a cutline in each overlapped area between two adjacent images, with minimum-squared differences of gray values at the same locations of the region in all image channels.
• Minimum Difference: Specifies the minimum-difference method. This method is suitable for most mosaicking projects. The algorithm determines a cutline in each overlapped area between two adjacent images, with minimum differences of gray values at the same locations of the region
• Minimum Relative Difference: Specifies the minimum-relative-difference method. This method is similar to Minimum Difference, but provides better output in cases where similar sections of data appear dissimilar in different images
• Edge: Specifies the edge-feature method. This method provides better output in urban-area mosaicking or in images containing many linear features. The objective of the Edge method is to avoid placing cutlines across linear features.
• Maximum Data: Specifies that cutlines is on the boundary of the real image pixels, meaning that NoData pixels is ignored when the image boundary is determined.
• Import: Uses the specified polygons as cutlines exactly as they appear in the vector file.
• File Extents: Specifies that the cutline is the extents of the input images and does not exclude NoData pixels from the cutline generation process.

[<vector_file>], [<field_name>], [<segment_number>]

• vector_file is the name of an existing vector layer to use as a constraint polygon. Using a constraining vector layer is useful when at least some of the input images include features, such as clouds, that you want to exclude from the final mosaic.
• field_name is the attribute field that contains the image source.
• segment_number is the number of the vector segment that contains the constraint polygon.

1. the value of its file level metadata tag: SourceID, or if that tag does not exist, then
2. the base name, without the extension, of the input source image's file name.

• Automatic: Determine whether constraints are required and, if so, applies one for each input image.
• Classic: Create one constraint polygon for each input image using PCI traditional algorithm that was developed for aerial images that are approximately square and contain a lot of overlap. You can adjust the option by specifying a value for the Thiessen Factor. If you do not specify a value, the default value of 50 is applied.
• Strips: Create one constraint polygon for each input image using PCI algorithm that was developed for projects that have a clear pattern of strips, such as often seen with ADS data. Images must be of similar size and align in at least one direction from, for instance, aerial flight lines or satellite swaths. Cutline-constraining polygons is generated for each image based on where it and its neighbors have actual data. This method first tries to group the images in rows or columns, and chooses the strips with the most consistent distance between images. It then removes the overlap between images in each strip using, at most, two neighboring images. Finally overlap is remove between the different strips leaving a constraining polygon. The extents of the polygons can be manipulated by specifying a value for the overlap Factor. If you do not specify a value, the default value of 20 is applied.
• No: Does not apply constraint polygons.

• Last Vector: Sets the last-created vector segment in the file specified for Global Cutline Avoidance Mask File as the global cutline-avoidance mask layer.
• Last Bitmap: Sets the last-created bitmap segment in file specified for Global Cutline Avoidance Mask File as the global cutline-avoidance mask layer.
• Specific Segment: Defines a specific segment in the file specified for Global Cutline Avoidance Mask File as the global cutline-avoidance mask layer. When you select this option, you must specify a value for Global Cutline Avoidance Mask Segment.

• Single Tile: Generates the output mosaic as a single tile.
• Vector: Use when you have an existing vector-polygon layer that contains the tile definitions you want to use for the mosaic. When you select this option, you can add options for it with Tile File, Segment Number, Field Name, and Buffer Distance, respectively.
• Dimensions: Creates tiles of a specific size. When you select this option, you can add options for it with Tile Height and Tile Width, respectively. When using this specification, the TileID values of the output tiles is generated using the convention <column_number>_<row_number>. For example, the upper-left tile always has a TileID of "1_1", the one immediately below is "1_2", and so on.
• Script File: Use when you have a text file that defines the coordinates of each tile you want to create. When you select this option, you can add options for it with Tile File.

• RASEXT: Raster extents
• GEOEXT: Geocoded extents
• LNGLATEX: Longitude and latitude extents
• RASOFFSZ: Raster offset and size
• GEOOFFSZ: Geocoded offset and size

• Geocoded Extents
• Geocoded Offset & Size
• Long/Lat Extents
• Raster Extents
• Raster Offset & Size

• CORNER: indicates that the values are relative to the upper-left corner of the first pixel in the output tile
• CENTER: indicates that the values are relative to the center of the first pixel

• Stride_X
• Stride_Y
• Ref_X
• Ref_Y

• SourceID: a text field that contains the SourceID of the source image.
• SourceFile: a text field that includes the full GDB string including path to the source input image.
• Z-order: an integer field that records the z-order of the predominate source image. The word "predominant" is used because, in the case of blending along cutlines, the source scene which had the largest contribution is the one indicated.