Source mosaic datasets

Source mosaic datasets are created primarily to simplify the data ingestion process and enable parameters to be refined prior to adding the source mosaic datasets to the derived mosaic datasets. The following are typical steps that are performed (which are discussed further below):

  1. Create a source mosaic dataset.
  2. Add the raster data (raster dataset, imagery, lidar).
  3. Define additional metadata.
  4. Compute cell sizes.
  5. Refine geometry.
  6. Refine footprints and NoData.
  7. Refine radiometry.
  8. Generate seamlines.
  9. Refine the mosaic dataset properties.
  10. Generate overviews.

Create a new mosaic dataset

Source mosaic datasets are created using the Create Mosaic Dataset tool. The spatial reference system, number of bands, and bit depth that are appropriate for the data being added should be specified. If all the source data will be in a single spatial reference system, then that is often used for the mosaic dataset. However, in most cases it is more appropriate to create the mosaic dataset in the spatial reference system that the derived mosaic dataset will be in, which should be sufficient to include all the possible data being added. This ensures that no additional densification of the footprints needs to be performed, which would potentially increase the number of vertices. Too many vertices (more than 100) can increase the time required to render clip the image, so it is generally advantageous to reduce the number where possible. For global datasets the Web Mercator Auxiliary Sphere projection is often used because it provides near-global coverage and results in pixel sizes that are in meters, which is often easier to understand than the decimal degrees used in the geographic WGS84. Note that all pixels are transformed directly to the spatial reference system of the client application. So having a mosaic dataset in a different spatial reference system from the source does not result in additional resampling and has a negligible effect on performance. If the number of bands and bit depth are not defined, they are taken from the first dataset that is added to the mosaic dataset. In the workflows, the bands and bit depth are defined when the mosaic dataset is created to ensure that they are set correctly.

Learn more about creating a mosaic dataset

Add rasters

Once a suitable source mosaic dataset has been created, the appropriate rasters are added. This uses the Add Rasters To Mosaic Dataset tool, which requires the use of the appropriate raster type and setting suitable parameters. This section in each workflow will detail recommendations appropriate for the workflow. Most of the raster types enable additional process functions to also be added. These functions may define stretches to enhance the imagery, or for satellite and aerial imagery, functions to orthorectify the data. In some cases, the georeferencing is only approximately defined. For example, imagery may be orthorectified initially using approximate navigation grade coordinates from an aircraft, but be refined at a later stage when the GPS/IMU data is fully processed.

The outcome of this step should be a mosaic dataset that enables you to start visualizing the mosaic dataset.

Learn more about adding rasters to a mosaic dataset

Metadata

Metadata is not only important to provide users with additional information about the rasters, but is used in queries and in defining the order for image display. Typically, metadata is ingested as part of the raster type when rasters are added. In some cases metadata cannot be extracted from the rasters, so it is added to the mosaic dataset as additional fields in the attribute table. You can do this by opening the table and adding new fields and using the Field Calculator to set the values, or by using the Add Field tool and Calculate Field tool to set the values. Alternatively, if metadata is available in other tables, the tables can be joined to the mosaic dataset and the fields copied across.

Learn more about adding metadata

Calculate cell sizes

The four cell sizes (LoPS, HiPS, MinPS, and MaxPS) in a mosaic dataset are used to define the available pixel sizes of a raster and the display scale range. LoPS is generally set to the resolution of the pixels on the ground and is computed when the imagery is added. When you use imagery that is not rectified or from a different source, each image may have a slightly different pixel size. In some workflows it is advisable to reset the LoPS value to a fixed value or the average of all similar rasters.

The HiPS value for imagery is used primarily to determine the scales at which overviews should be computed. In most workflows, the pixel size at which overviews are created (if required) is explicitly set; therefore, this value is not very important. The most appropriate value for HiPS depends on the availability of pyramids and the width of the imagery. If automatically set, it is possible that images with the same pixel size, but different widths, will end up having different HiPS values. Therefore, it is sometimes advantageous to set the HiPS value appropriate to the data source depending on the typical number of pyramid levels that are useful. An approximate value for this would equal image width/1,000.

In the generic workflow, the MinPS and MaxPS cell sizes are automatically determined and computed when the data is added and can be reset by using the Calculate Cell Size Ranges tool. The Calculate Cell Size Ranges tool computes cell sizes by determining the overlap between images and assumes that images with a larger pixel size should be displayed at the smaller scales. In many workflows these rules do not hold, and often it is more appropriate to set the MinPS value for all images to 0 and the MaxPS value dependent on the pyramids available and the scales to which the images should be visible. Therefore, in many of the workflows the MinPS and MaxPS values are explicitly set.

For more information on cell sizes, see Cell size ranges in a mosaic dataset.

Refine geometry

Some workflows require an improved geometric accuracy of the imagery. This step is typically performed on the source mosaic dataset. The Refine geometry section of the workflows defines how this can best be achieved. Typically, when orthorectifying imagery, the imagery can be added using approximate orientation data and elevation models, and later these values are refined. Similarly, when adding satellite scenes that have already been orthorectified, it may be necessary to refine the geometry to make it more accurate.

Footprints and NoData

When rasters are added to a mosaic dataset, the footprints are computed based on the available metadata, which is typically the envelope boundary of the rasters. The correct definition of footprints in a mosaic dataset is important, as they are not only used for identifying the extent of the imagery, but can also be used to clip out parts of an image that are not to be displayed.

If the geometry of a raster has been refined after it has been added, it is necessary to recompute the footprint.

For mosaic datasets created from preprocessed rectangular images, using only the envelope of the raster is sufficient. In such cases, the footprints are only used to enable the system to quickly find the appropriate rasters. If there are NoData values, they can be defined as a property of the raster or a NoData mask can be defined for each raster. When the system identifies NoData pixels covering a request extent, the system will look for the next most suitable raster for additional pixels. As long as there are not too many overlapping rasters, this works well.

In cases where there are a lot of overlapping rasters, the pixel-based NoData can become a bottleneck, and it is better to turn off the NoData pixels and clip rasters by their footprints. Using a footprint to define NoData is more efficient, because the system can quickly perform an analysis of the overlapping footprint geometries to determine what rasters are required. There are properties of a mosaic dataset that define how the system handles NoData and clipping. These are defined in more detail in the Mosaic dataset properties step.

Footprints generally need to be recomputed for images that are not premosaicked into tiles. Such imagery will generally have a border of NoData values within the envelope of the rasters, and the Build Footprints tool can be used to refine the footprints. This tool has different modes. The radiometry mode creates a mask based on ranges of NoData values and generates a contour around the mask. There is a range of parameters that control how such masks are created, and often a very precise footprint cannot be obtained. In such cases, it is common to shrink the footprints a bit to exclude the edge pixels. For most sensors these edge pixels are of little value and clipping them out is advantageous. For images that complete frames with no NoData pixels and are orthorectified by the system, the footprint can be computed based on the geometry mode. This performs an image-to-ground transformation for the corners and edges of the frames.

Learn more about recomputing footprints radiometrically

Footprints also need to be computed for rasters that contain large NoData areas. In some workflows it is necessary to manually edit footprints or import footprints from different vector sources, for example, to clip imagery to specified extents such as county boundaries or exclude large expanses of water or clouds.

Similar to footprints, each mosaic dataset has a boundary that defines the extent. By default, the system computes this by performing an intersect of all the footprints. This can result in very complex boundaries, so workflows may recompute the footprint to be only an extent or be simplified.

Each of the detailed workflows will provide information on how best to refine the footprints and set NoData values.

Learn more about recomputing footprints

Refine radiometry

Unless the rasters are categorical, elevation, or already enhanced, it is often necessary to enhance the radiometry of the imagery to increase the interpretability or make the image more suitable for analysis. Typically, appropriate stretch functions are added as part of a source mosaic dataset, or in some workflows the parameters for stretching the imagery are computed as part of the source mosaic dataset and applied when creating the derived mosaic dataset. Each workflow will describe the most suitable method for defining such enhancements.

Statistics must be computed to determine the stretch parameters. These statistics may have been computed as a preprocess and stored with the rasters. Alternatively, statistics can be computed on the mosaic dataset items. This is typically performed if the item contains functions such as pan-sharpening that would change the statistics (from their original raw data format). Computing statistics on the raster items in the mosaic dataset has the advantage that clipping of the footprints is also applied; which, in some workflows, ensures that nonrequired border areas are removed from the statistics computations.

Learn more about statistics raster data and in a mosaic dataset

Seamlines

If the output of a workflow required a seamless image, the workflow may require that seamlines are created as part of the source mosaic datasets. Typically, these are computed for satellite and aerial imagery based on determining suitable areas for blending overlapping imagery. Note that seamline generation will group rasters together based on the Cell Size Tolerance Factor, which is set in the mosaic dataset properties. Prior to generating seamlines is it often necessary to set this parameter and run the Compute Cell Size Ranges tool. When running this tool, you should turn off the Compute Minimum and Maximum Cell Sizes parameters so that the pixel sizes are not recomputed.

The seamline generation makes use of the full overlapping extent of imagery, and there are cases where imagery needs to be prioritized or the seamlines should be computed only for a smaller subset of the overlapping areas. In these cases the workflows may temporarily reset the footprints to the required extents before seamline generation. For scanned maps, seamlines are typically set to the map data extent by importing the sheet cutlines of the original maps (to remove any map marginalia along the outside of the map).

Learn more about seamlines

Mosaic dataset properties

There are many properties that can be set on mosaic datasets. Source mosaic datasets are generally not directly used as image services; therefore, their properties are not as important to set as derived mosaic datasets. The primary reason to set properties for the source mosaic datasets is to enable quality assurance checking of the mosaic datasets.

More extensive details on setting properties will be covered in the section on Derived mosaic datasets. Most workflows will set all the required properties to ensure suitable quality assurance.

Learn about mosaic dataset properties

Overviews

Overviews are often created to enable faster viewing of mosaic datasets at smaller scales. In some workflows the overviews are not created and smaller-scale images may be used instead. Although overviews speed up display at smaller scales, they do hinder the use of the mosaic methods at these scales, which limits the user's ability to change the order of the imagery. Therefore, the scale at which they are created needs to be chosen carefully.

Overviews should be computed after the mosaic datasets properties are set so the appropriate default mosaic method rules are used in the overview creation.

In many workflows, overviews are computed from source mosaic datasets, then added to the derived mosaic datasets. When attributed correctly, they allow users to view collections of imagery at small scales by setting appropriate filters.

By default, ArcGIS determines the pixel size for generating the overviews by identifying the HiPS values of the underlying images. This can result in the pixel sizes varying in different areas. Therefore, most workflows set a specific pixel size at which the overviews should be created.

Learn more about overviews

10/28/2013