Generate ortho mapping products using the custom wizard—ArcGIS Pro

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:


Option	Description
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


Parameter	Description
Matching Method	The matching method for generating a point cloud: Extended Terrain Matching (ETM)—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. This is the default. Semi-Global Matching (SGM)—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 Multi-View Matching (MVM)—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 to Filter (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 (in meters)	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 defines the minimum angle the stereo pair must meet. The default is 5 degrees. 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 defines the maximum angle the stereo pair must meet. The default is 70 degrees. 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.
Advanced Options
Maximum Omega/Phi Difference (in degrees)	The maximum threshold for the Omega and 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. The default value is 2.
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 in 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:

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.
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


Parameter	Description
Surface Type	Create a digital terrain model or a digital surface model. Digital Terrain Model—Create a digital terrain model by interpolating only the raster surface using ground only points. Digital Surface Model—Create a digital surface model by interpolating a raster using all the points; both ground and above around points. This is the only available Surface Type option if you selected Interpolate DEM from Solution Points on the Product Generation Settings page.
Cell Size	The pixel size for the DEM. The output pixel size should be the same or larger than the pixel size of source image. You can enter a value to be multiplied by the Ground Sample Distance (GSD), which is the source pixel resolution, or you can enter an explicit Value which represents the size of the pixel in units that match the source imagery.
Format	The format for the output raster dataset: Cloud Raster Format TIFF Format Meta Raster Format
Compression	The compression method for the output DEM. The following options will be available based on the Format you choose: None LZW JPEG JPEG2000 LERC
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. This is the default. 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	The 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. This is the default. 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	This parameter will update the orthorectification process in the image collection. Check the checkbox to orthorectify the image collection using the DEM generated using the wizard. If you don't want to replace the current elevation model with the newly generated DEM, uncheck the checkbox. 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 have another DEM to use to orthorectify the image collection, you can enter it on the Orthomosaic Settings page.

Color Balance Settings page

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

Parameters for Color Balance Settings


Parameter	Description
Select Mosaic Candidates	The Select Mosaic Candidates parameter is typically used for image collection with dense overlaps such as drones. It is 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.
Balance Method	The color balancing algorithm to use. Dodging—Image pixel values will be adjusted toward a target color surface using a dodging window and the corresponding gamma function established from the local statistics 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. This is the default. Histogram—Image pixel value will be adjusted by matching the histogram of each image to the histogram of the entire 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—Image pixel value will be adjusted by matching the histogram within one standard deviation between each image and the entire 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	If the Dodging balance method is used, each pixel needs a target color, which is determined by the surface type. Single color—All the pixels will be altered toward a single color point, which is the average of all pixels. This technique is appropriate when there are only a small number of images and a few types of ground objects. If there are too many rasters or there are too many types of ground surface features, the output color may become blurred. Color grid—Pixels will be altered toward a grid of color points that are distributed across the image collection. This technique is appropriate when you have a large number of images or areas with a large number of diverse ground objects. First order—A color surface (a slanted plane) will be 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 will be 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. This is the default. Third order—A color surface will be represented as a third 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 multiple points obtained from the cubic surface. The result is similar to that of Color grid.
Target Raster	The raster that will be used 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, will be derived from this target image.
Recalculate Statistics	Once color balancing has been performed, there may be new pixel values in your raster. Check the check box to calculate the statistics with the latest pixel values.
Number of Columns to Skip	The number of horizontal pixels to skip between the samples used to calculate statistics. The value must be greater than zero and less than the number of columns in the raster. The default is 1.
Number of Rows to Skip	The number of vertical pixels to skip between the samples used to calculate statistics. The value must be greater than zero and less than or equal to the number of rows in the raster. The default is 1.

Seamline Settings page

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

Parameters for Seamline Settings


Parameter	Description
Computation Method	The computation method that will be used to generate seamlines: Disparity—Seamlines will be generated based on the disparity images of stereo pairs. This method can avoid seamlines cutting through buildings. Voronoi—Seamlines will be generated using the area Voronoi diagram. This is the default. Radiometry—Seamlines will be generated based on the spectral patterns of features within the imagery. Edge Detection—Seamlines will be generated over intersecting areas based on the edges of features in the area. Geometry—Seamlines will be generated for overlapping areas based on the intersection of footprints. Areas with no overlapping imagery will merge the footprints.
Advanced Options
Pixel Size	The pixel size to use for seamline generation. Sometimes, a mosaic dataset contains raster items with different resolutions. Use this parameter choose the pixel size for generating seamlines.
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.
Processing
Blend Width Units	The unit of measurement to use 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	The method that will be used to blend one image into another over the seamlines. The options are to blend inside the seamlines, outside the seamlines, or both inside and outside. Both— Pixels on either side of the seamlines will be blended. For example, if the Blend Width value is 10 pixels, 5 pixels will be blended on the inside and the outside of the seamline. This is the default. Inside—Pixels inside the seamline will be blended. Outside—Pixels outside the seamline will be blended.
Request Size Type	The units that will be used for the Request Size value. Pixels—The request size will be modified based on the pixel size. This option resamples the closest image based on the raster pixel size. This is the default. Pixel scaling factor—The request size will be modified 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.
Sliver Removal Options
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	The maximum size a polygon can be to still be considered a sliver. This parameter is specified in pixels and is based on the Request Size value, 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.

Orthomosaic Settings page

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

Parameters for Orthomosaic Settings


Parameter	Description
Pixel Size	The pixel size that will be used for the orthomosaic.
Format	The output format that will be used for the orthomosaic: Cloud Raster Format TIFF Format JPEG Format JPEG2000 Format Meta Raster Format
Compression	The compression method that will be used for the output. The following options will be available based on the Format value you choose: None LZW JPEG JPEG2000 LERC
Optional
Orthorectify Source Image Collection Using	The digital elevation model (DEM) that will be used to orthorectify the orthomosaic. Options include the reference DEM for the workspace, a DEM generated using the DEMs Wizard, or an external DEM.
Only Orthorectify Source Image Collection	Checked—Only the individual images in the image collection are orthorectified. An orthomosaic is not generated. Unchecked—An orthomosaic is generated.