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Generate ortho mapping products using the custom wizard

Available with Advanced license.

The Custom Products wizard provides a flexible guided workflow for generating your ortho mapping products. For example, you can generate a digital elevation model (DEM) and an orthomosaic at the same time, using the Custom Products wizard. You can also generate a mosaic by disabling steps that are not needed for your workflow, such as turning off color balancing if the image colors are already homogeneous.

Product Generation Settings page

The Product Generation Settings page allows you to specify the ortho mapping products you want to create. The wizard allows you to create a digital elevation model (DEM), an orthomosaic, or both. Check the products you want to create. Each ortho mapping product also has options to perform specific tasks during its creation. The following table lists the options on the Product Generation Settings page:

OptionDescription

Digital Elevation Model

Check this box to create a DEM ortho product.

You will need to complete the options in the Point Cloud Settings page. If you do not check the Interpolate DEM from Solution Points check box, you will also need to complete the options in the DEM Interpolation Settings page.

Interpolate DEM from Solution Points

Check this box to create your DEM by interpolating from solution points. When you check this box, you only need to complete the Point Cloud Settings page.

Orthomosaic

Check this box to create an orthomosaic product.

Generate Seamlines

Check this box to build seamlines during the orthomosaic creation. If you choose to build seamlines, you will need to complete the Seamline Settings page.

Color Balance

Check this box to color balance the image collection during the orthomosaic creation. If you choose to color balance, you will need to complete the Color Balance Settings page.

Generate Orthomosaic

Check this box to create a raster dataset output for the orthomosaic. If you choose this option, you will need to complete the Orthomosaic Settings page.

Point Cloud Settings page

The following table lists the parameters on the Point Cloud Settings page:

Parameters for Point Cloud Settings

ParameterDescription

Matching Method

There are three matching methods for generating a point cloud:

  • ETM (Extended Terrain Matching)—Feature-based stereo matching that uses the Harris operator to detect feature points. Since fewer feature points are extracted, this method is fast and can be used for data with minimal terrain variations and detail.
  • SGM (Semi-Global Matching)—Produces points that are denser and with more detailed terrain information. It can be used for images of urban areas. This is more computational intensive than ETM.1
  • MVM (Multi-View Matching)—Based on the SGM matching method followed by a fusion step where the redundant depth estimations across single stereo model are merged. It produces dense 3D points and is computationally efficient.2

Maximum Object Size (in meters)

A search radius used to filter out objects above ground. Objects smaller than the threshold are filtered as ground; otherwise objects are treated as above-ground features such as buildings, bridges, or trees.

Point Ground Spacing

Defines the spacing, in meters, at which the 3D points are generated.

The suggested spacing is five times the source image pixel size.

Minimum Intersection Angle (in degrees)

The point cloud is generated from stereo pairs. This value, in degrees, defines the minimum angle the stereo pair must meet.

A stereo pair with an intersection angle that is too small will produce unstable results when triangulating 3D points.

Maximum Intersection Angle (in degrees)

The point cloud is generated from stereo pairs. This value, in degrees, defines the maximum angle the stereo pair must meet.

A stereo pair with an intersection angle that is too large will produce few or no match points.

Minimum Area Overlap

The percentage of overlapping area over the entire image. The default is 0.6.

Maximum Omega/Phi Difference (in degrees)

The maximum threshold for the Omega/Phi difference between the two image pairs. The Omega values and Phi values for the image pairs are compared. If the difference between either the two Omega or the two Phi values is above the threshold, the pairs will not be formatted as a stereo pair.

Maximum GSD Difference

The threshold for the maximum ground sample distance (GSD) between two images in a stereo pair. If the resolution ratio between the two images is greater than the threshold value, the pairs will not be built as a stereo pair.

Number of Image Pairs

The number of pairs used to generate 3D points. For a project that has dense overlaps and many stereo pairs, increasing this number means more computation time. The suggested value is 4.

Sometimes a location may be covered with many image pairs. In this case, the tool will order the pairs based on the various threshold parameters specified within this tool. The pairs with the highest scores will be used to generate the points.

The parameters that affect the order of the stereo pair, in addition to Minimum Intersection Angle, Maximum Intersection Angle, and Minimum Area Overlap, can also include Maximum Omega / Phi Difference, Maximum GSD Difference, and Adjustment Quality Threshold.

Adjustment Quality Threshold

Specify the minimum adjustment quality that is acceptable. The threshold value is compared to the adjustment quality value that is stored in the stereo model. Image pairs with an adjustment quality less than the specified threshold receive a score of 0 for this criteria and will descend in the ordered list. The values for the threshold ranges from 0 to 1. The suggested value is 0.2.

References:

  1. Heiko Hirschmuller et. al., "Memory Efficient Semi-Global Matching," ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume 1-3, (2012): 371-376.
  2. Hirschmuller, H. "Stereo Processing by Semiglobal Matching and Mutual Information." Pattern Analysis and Machine Intelligence, (2008).

DEM Interpolation Settings page

The following table lists the parameters on the DEM Interpolation Settings page:

Parameters for DEM Interpolation Settings

ParameterDescription

Surface Type

Create a digital terrain model or a digital surface model.

  • DTM—Create a digital terrain model by interpolating only the raster surface using ground only points.
  • DSM—Create a digital surface model by interpolating a raster using all the points; both ground and above around points.

Cellsize

The cell size of the output raster dataset.

Format

The format for the output raster dataset:

  • Cloud Raster Format
  • TIFF Format
  • Meta Raster Format.

Interpolation Method

The method used to interpolate the output raster dataset from the point cloud.

  • TIN Linear Interpolation—Also known as triangulated irregular network (TIN) linear interpolation designed for irregularly distributed sparse points, such as solution points from block adjustment computation.
  • TIN Natural Neighbor Interpolation—This is similar to triangulation but generates a smoother surface and is more computationally intensive.
  • Inverse Distance Weighted Average Interpolation—This is used for regularly distributed dense points, such as point cloud LAS files from the Generate Point Cloud tool. The IDW search radius is automatically computed based on average point density.

Smoothing Method

Select a filter to smooth the output raster dataset.

  • Gaussian 3 by 3—A Gaussian filter with a 3 by 3 window.
  • Gaussian 5 by 5—A Gaussian filter with a 5 by 5 window.
  • Gaussian 7 by 7—A Gaussian filter with a 7 by 7 window.
  • Gaussian 9 by 9—A Gaussian filter with a 9 by 9 window.
  • No smoothing—No smoothing filter is applied.

Fill Missing Pixels Using

A DEM input that is used to fill NoData areas.

Areas of NoData may exist where stereo overlap is insufficient, or no matching points are found in the area during point cloud generation.

Orthorectify images using DEM

The generated DEM will be used to refine the orthorectification of the image collection.

If you don't want to replace the current elevation model with the newly generated DEM, uncheck the option.

This parameter will update the orthorectification process in the Image Collection. Check the check box only when you are sure that the DEM output is acceptable and you do not need to generate it again. If you generated a DEM without selecting this parameter, you can apply the resulting DEM for orthorectification when you use the Orthomosaic wizard.

Seamline Settings page

The following table lists the parameters on the Seamline Settings page:

Parameters for Seamline Settings

ParameterDescription

Select Mosaic Candidates

The Select Mosaic Candidates parameter is typically used for image collection with dense overlaps such as drones. It's used to find an optimum set of images that can be used in mosaicking images. The selected images will be used in seamline building, color balancing, and output mosaic operation.

Check the check box to perform the selection of the best image candidates to use.

Maximum Overlap Area

The maximum amount of overlap that you want between the mosaic dataset and the footprint of each image in the mosaic dataset. If the percentage of overlap is higher than this threshold, the image is excluded since it will have too much redundant information.

The percentage is expressed as a decimal. For example, a maximum overlap of 60 percent is expressed as 0.6.

Maximum Area Loss Allowed

This is the maximum percentage of area that can be excluded by the candidate images. After the tool chooses the best candidate images based on the Maximum Area Overlap, it will then check to see if the maximum excluded area is below the threshold specified. If the excluded area is greater than the specified threshold, the tool will add more candidate images to fill in some of the voids that were missing. Most of these excluded areas will likely be along the border of the mosaic dataset.

The percentage is expressed as a decimal. For example, a maximum excluded area of 5 percent is expressed as 0.05.

Computation Method

Choose the computation method to use to generate your seamlines:

  • Geometry—Generate seamlines for overlapping areas based on the intersection of footprints. Areas with no overlapping imagery will merge the footprints.
  • Radiometry—Generate seamlines based on the spectral patterns of features within the imagery.
  • Edge Detection—Generate seamlines over intersecting areas based on the edges of features in the area.
  • Voronoi—Generate seamlines using the area Voronoi diagram.
  • Disparity—Generate seamlines based on the disparity images of stereo pairs. This method can avoid seamlines cutting through buildings.

Cellsize

Generate seamlines for raster datasets that fall within the following range of spatial resolutions.

Minimum Region Size

Specify the minimum region size, in pixel units. Any polygons smaller than this specified threshold will be removed in the seamline result. The default is 100 pixels.

Blend Width Units

Specify the unit of measurement for blend width.

  • Pixels—Measure using the number of pixels. This is the default.
  • Ground units—Measure using the same units as the mosaic dataset.

Blend Width

Blending (feathering) occurs along a seamline between pixels where there are overlapping rasters. The blend width defines how many pixels will be blended.

If the blend width value is 10, and you use BOTH as the blend type, then 5 pixels will be blended on the inside and outside of the seamline. If the value is 10, and the blend type is INSIDE, then 10 pixels will be blended on the inside of the seamline.

Blend Type

Determine how to blend one image into another, over the seamlines. Options are to blend inside the seamlines, outside the seamlines, or both inside and outside.

  • Both— Blend using pixels on either side of the seamlines. For example, if the Blend Width is 10 pixels, 5 pixels will be blended inside and 5 pixels will be blended outside the seamline. This is the default.
  • Inside—Blend inside the seamline.
  • Outside—Blend outside the seamline.

Request Size Type

Set the units for the Request Size.

  • Pixels—Modify the request size based on the pixel size. This is the default, which resamples the closest image based on the raster pixel size.
  • Pixel scaling factor—Modify the request size by specifying a scaling factor. This option resamples the closest image by multiplying the raster pixel size (from cell size level table) with the pixel size factor.

Request Size

Specify the number of columns and rows for resampling. The maximum value is 5,000. Increase or decrease this value based on the complexity of your raster data. Greater image resolution provides more detail in the raster dataset but also increases the processing time.

Minimum Thinness Ratio

Define how thin a polygon can be, before it is considered a sliver. This is based on a scale from 0 to 1.0, where a value of 0.0 represents a polygon that is almost a straight line, and a value of 1.0 represents a polygon that is a circle.

Slivers are removed when building seamlines.

Maximum Sliver Size

Specify the maximum size a polygon can be and still be considered a sliver. This parameter is specified in pixels and is based on the Request Size, not the spatial resolution of the source raster. Any polygon that is less than the square of this value is considered a sliver. Slivers are removed when building seamlines.

Color Balance Settings page

The following table lists the parameters on the Color Balance Settings page:

Parameters for Color Balance Settings

ParameterDescription

Balance Method

The color balancing algorithm to use.

  • Dodging—Adjust image pixel values toward a target color surface using a dodging window and the corresponding gamma function established from the local statics of the window and the target color surface. The color surface can be calculated from the input image collection with the specified color surface type or from an external target raster.
  • Histogram—Adjust image pixel value by matching the histogram of each image and the histogram of the total image collection or the histogram of the external target raster if specified. This technique works well when all of the images to be color balanced have a similar histogram.
  • Standard deviation—Adjust image pixel value by matching the histogram within one standard deviation between each image and the whole image collection or the target raster if specified. This technique works best when all of the images to be color balanced have normal distributions.

Color Surface Type

When using the Dodging balance method, each pixel needs a target color, which is determined by the surface type.

  • Single color—Use when there are only a small number of images and a few different types of ground objects. If there are too many rasters or too many types of ground surface features, the output color may become blurred. All the pixels are altered toward a single color point, which is the average of all pixels.
  • Color grid—Use when you have a large number of images or areas with a large number of diverse ground objects. Pixels are altered toward a grid of color points that are distributed across the image collection.
  • First order—A color surface (a slanted plane) represented as a first order polynomial. This technique tends to create smoother color changes and uses less storage in the auxiliary table, but it may take longer to process compared to the color grid surface. All pixels may be altered toward many points obtained from the two-dimensional polynomial slanted plane. The result is similar to that of Single color.
  • Second order—A color surface represented as a second order polynomial. This technique tends to create a smoother color change and uses less storage in the auxiliary table, but it may take longer to process compared to the color grid surface. All input pixels are altered toward a set of multiple points obtained from the two-dimensional polynomial parabolic surface. This method produces an intermediate result between that of Single color and Color grid.
  • Third order—This technique tends to create a smoother color change and uses less storage in the auxiliary table, but it may take longer to process compared to the color grid surface. All input pixels are altered toward multiple points obtained from the cubic surface. The result is similar to that of Color grid

Target Raster

The raster you want to use to as a target to color balance the image collection. It can be a raster dataset, a mosaic dataset, or an image service. The statistics required by the balance method and color surface type, if applicable, are derived from this target image.

Recalculate Statistics

Once color balancing has been performed, there may be new pixel values within your raster. Check the check box to calculate the statistics with the latest pixel values.

Orthomosaic Settings page

The following table lists the parameters on the Orthomosaic Settings page:

Parameters for Orthomosaic Settings

ParameterDescription

Pixel Size

The pixel size for the output orthomosaic.

The output pixel size should be the same or larger than the pixel size of the source image.

Format

The output format for the output orthomosaic.

  • Cloud Raster Format
  • TIFF Format
  • JPEG Format
  • JPEG2000 Format
  • Meta Raster Format

Compression

The compression method for the output

  • None
  • LZW
  • JPEG
  • LERC
  • JPEG2000

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