Imagery: Data management patterns and recommendations

A mosaic dataset is a collection of images (raster datasets) stored as a catalog and viewed as a mosaicked image. These collections can be extremely large both in total file size and number of raster datasets.

Mosaic datasets are unique because they perform dynamic mosaicking using mosaic methods that can be modified by the user of the mosaic dataset. They also allow you to add functions on the entire dataset or for each image, which are applied on-the-fly as the imagery is accessed and the mosaicked image is generated. These features and others affect how you manage your data with a mosaic dataset.

General principles

When you manage imagery your goal is to provide end users with best available imagery according to their requirements. You can achieve this goal using a mosaic dataset, which helps you to:

As a general rule, users of imagery prefer to have a single comprehensive source of imagery for their application rather than having to look for imagery from multiple sources. So, one rule of thumb that will be promoted in this document is to reduce the number of mosaic datasets that are published and accessible to users. Because, by reducing the number of mosaic datasets to a user, their search for imagery is reduced and they can use the same mosaic dataset for multiple applications-simplifying maintenance and application development.

Users should generally connect to one imagery source that displays the most appropriate imagery for their needs. They should be able to access suitable metadata and refine aspects, such as the compression for transmission, order of the imagery, or lock to a specific image. Different mosaic datasets may be published to define different types of data (such as, natural color imagery, false color imagery, or elevation), but generally the published mosaic datasets should not be specific to a geography, type of sensor, or date range.

Typical examples of published mosaic datasets include:

Imagery sources

Imagery can come from a variety of sources, such as aerial or satellite sensors, scanned maps, or the output from analysis. It can be panchromatic, multispectral, thermal, elevation, or thematic. It can be stored as files on disk or file storage system (such as NAS or SAN), within a geodatabase, or accessed through a service (such as an image service or Web coverage service (WCS)).

Imagery and rasters are added to a mosaic dataset according to its raster type. The raster type is designed to understand the file format and specific information about a product. Essentially, the raster type simplifies the processing of adding complex image data to a mosaic dataset.

In ArcGIS there are several different raster types, some for specific image products, and others for specific image sensors, such as Landsat 7, WorldView-2, or IKONOS. The raster type identifies metadata, such as georeferencing, acquisition date, and sensor type, along with a raster format.

By adding raster data according to a raster type, the appropriate metadata is read and used to define any processing that needs to be applied. For example, when adding a QuickBird Standard scene, a scene may be defined by an .imd file. This file contains metadata information about the raster dataset and may point to one or more .tif files. To add this data correctly, you will use the QuickBird raster type because it searches for this combination of file types. If you added this data as a regular raster dataset, then only the .tif files will be recognized and added, and any metadata information that would affect the functions needed or the georeferencing would be missing.

Raster types help with automation because they define how the imagery should be setup within the mosaic dataset. You can create your own through code or modifying the properties of an existing raster type and saving it.

It is important that you use the correct raster type to add your imagery to a mosaic dataset. You may need to examine the files and their metadata sources to identify the file format or image product that is identified using the raster type.

Functions that define processing can also be added after imagery has been added to a mosaic dataset. This is often done to convert the output to a particular image product or to apply corrections to individual images. The functions can be applied to individual images or to the entire mosaic dataset.

Direct access

No matter what mosaic dataset configuration you are implementing, you must make sure that the imagery is readable; otherwise, the mosaic dataset will not be able to display the imagery. The location of the imagery is identified in a hard-coded path, therefore if you move the imagery you must update the mosaic dataset and vice versa.

When preprocessing is necessary

Managing and publishing imagery using a mosaic dataset can save you time over traditional methods of mosaicking image collections together or producing multiple outputs; however, there are times when you want to consider some preprocessing. The recommended preprocessing applies to creating the fastest and best mosaicked imagery display.

Build pyramids— Pyramids help to improve the display speed of imagery; however, they can also impact the number of mosaic dataset overviews that may need to be generated. Generally, you should build pyramids for images with greater than 3000 columns. There is little to gain from building pyramids for a collection of preprocessed and tiled imagery as overviews generally provide a better solution for improving performance.

Calculate statistics—Statistics are used by the renderer when the imagery is stretched for displayed. Without statistics you may see a black or very dark image when working with non enhanced imagery. Generally, you should calculate statistics for imagery that is not enhanced (radiometrically). For example, many ortho photos are enhanced as part of their processing (such as NAIP or DOQQ); therefore, you do not need to calculate statistics. Whereas, raw imagery or imagery from a satellite is generally not enhanced; therefore, to make sure it displays well, you should calculate the statistics. Statistics don't always need to be calculated from every pixel; therefore, you can increase the speed at which they're calculated by specifying a skip factor. One way to identify a reasonable skip factor value is to divide the number of columns by 1000 and use the quotient (integer) as the skip factor.


Two tools are recommended for building pyramids and calculating statistics. There are two check boxes on the Add Rasters To Mosaic Dataset tool to build the pyramids and statistics as part of the procedure to add the imagery to the mosaic dataset. Otherwise, use the Build Pyramids And Statistics tool, which can be run on a workspace of data or mosaic dataset. This can be run before adding the imagery to the mosaic dataset or after. If you are going to build pyramids, be sure to build these before defining or building overviews on the mosaic dataset.

Optimized image formats— Some imagery can be slower to read than others due to their storage format or compression and it is recommended that you convert these into more optimal formats. For example, an ASCII DEM image format is slow to read; therefore, it is recommended that you convert it to a format such as TIFF. Also, if the image is very large and not tiled it is recommended that you convert this to a tiled TIFF format to optimize disk access. Also, when converting imagery consider using either of lossless (e.g. LZW) or lossy (e.g. JPEG) compression. You could choose to use a wavelet based compressions, such as JPEG 2000, but these are generally more CPU intensive to decompress while providing only marginally better compression. When converting imagery isn't an option you can build overviews on the mosaic dataset, that start at a very low pixel size (using the Define Overviews tool).

Overview of mosaic dataset configurations

The basic design for a mosaic dataset is one mosaic dataset containing a collection of imagery. In this design, each image or raster dataset is added as an individual item in the mosaic dataset and represented as a row in the attribute table.

Basic configuration

Understanding that a mosaic dataset is both a table and a dynamically mosaicked image is important. Because how you create the mosaic dataset and its table will impact the mosaicked image, and how you want the image to appear is impacted by the design of the mosaic dataset and its attribute table.

It is generally recommended that you manage your imagery within a mosaic dataset, but that you use another mosaic dataset (a reference mosaic dataset) to share or disseminate (publish) the contents. By using a reference mosaic dataset, users cannot accidently make modifications to your mosaic dataset such as adding or removing imagery.

Organizational types of mosaic datasets

The organization of your mosaic datasets can become more complex as you need to manage different types of data. Below, illustrates two standard combinations that could be used to manage and publish your imagery.

General configurations

It is generally advantageous to separate mosaic datasets into two types; those which are primarily used for management and those which are published. This separation can aid in organization.

As you build and organize your collection of imagery using mosaic datasets it may be useful to understand the different types of mosaic datasets and what purpose they may serve.

Source mosaic dataset

Used for managing imagery. It generally contains a collection of similar imagery. You may use a number of these source mosaic datasets to manage different collections. These can be published directly or used as the source for other mosaic datasets. It is recommended that you provide access to (publish) this mosaic dataset using a reference mosaic dataset to keep it secure.

A source mosaic dataset is created using the Create Mosaic Dataset tool. If the input imagery has a consistent bit depth or number of bands, then these values do not need to be defined in this tool as they'll be taken from the first image added. The spatial reference system will likely be the same as the inputs, but if the input data spans multiple spatial reference systems then choose an appropriate one for all. Then use the Add Rasters To Mosaic Dataset tool and use the appropriate raster type.

In most cases the images in a source mosaic dataset will have same number of bands and bit depth. These source mosaics are managed and used to refine aspects of the collection, such as refining the footprints or setting processes such as orthorectification.

You can modify the functions for individual images by accessing the Viewer window for each through the attribute table, or modifying more than one using the Raster Functions Editor Wizard accessed from the Footprint layer in ArcMap's table of contents.

Generally, if this imagery represents a single dataset such as imagery covering a specific date, then you will build overviews for this mosaic dataset.

Derived mosaic dataset

This is used to define collections of imagery often viewed by users as a single collection. The source of a derived mosaic dataset is generally one or more source mosaic datasets. For example, this could be a collection of all natural color imagery, with the source coming from multiple source mosaic datasets. It is recommended that you provide access to (publish) this mosaic dataset using a reference mosaic dataset to keep it secure. Additionally, you can create other mosaic datasets from this to provide specific imagery products, such as a specific band combination, or only over a specific area.

A derived mosaic dataset is also created using the Create Mosaic Dataset tool. Often the input imagery will have various bit depths and number of bands; therefore, you should specify these values. Choose the bit depth and number of bands that define the output product. Additionally, choose a spatial reference system that can accommodate all the imagery.

The spatial reference system is used to generate the footprints, boundary, and other related items in the mosaic dataset, as well as a default with which the mosaicked imagery will be resampled. You should choose one that is suitable for all the imagery you may add. This could be a country system or UTM zone. However, if you're creating a mosaic dataset that may be global in extent or will be mashed up with web services you may want to use the WGS 1984 Web Mercator Auxiliary projection.

Rasters are added to a derived mosaic dataset using the Table raster type to add the imagery from the source mosaic datasets. By using the Table raster type you will create a mosaic dataset containing all or a selection of the table items in the source mosaic datasets. This will give you the ability to perform any necessary queries. Functions can be added as the images are added that can transform or select specific parts of the imagery. For example, a function may convert 16-bit imagery to 8-bit, or extract specific bands from a multispectral source.

Additionally, the Synchronize Mosaic Dataset tool can be used to update this mosaic dataset if any of the sources have been modified, such as modified footprints or new imagery that has been added.

If you add the source mosaic datasets using the Raster Dataset raster type instead of the Table raster type, then each source mosaic dataset will be represented as a single item within the derived mosaic dataset, thereby limiting your ability to perform queries and identify metadata to the source mosaic dataset, and not each image within it.

Generally, you will not build overviews for this mosaic dataset as the overviews will exist within the source mosaic datasets. However, it may be necessary to build them if the derived mosaic dataset covers a much greater extent than each source. One option is to use another image or image service to provide imagery coverage for the full extent of the mosaic dataset. When adding this image you may want to uncheck the option to build the boundary as the boundary will be extended to the extent of this image, which may not be ideal.

Referenced mosaic dataset

This behaves similarly to a regular mosaic dataset; however, you cannot add additional rasters to the mosaic dataset, you cannot build overviews, and you cannot calculate the pixel size ranges. You can redefine the boundary, for example, to restrict access to specific areas or define additional functions to be applied to all imagery. It is used to provide access to mosaic datasets (or serve raster catalogs as image services) with different mosaic dataset-level functions. Sharing access to a referenced mosaic dataset ensures that those accessing it cannot make modifications to the source or derived mosaic datasets, which could impact other users.

Reference mosaic datasets are created using the Create Referenced Mosaic Dataset tool and by defining another mosaic dataset as the source. Typically this source could be a source mosaic dataset or a derived mosaic dataset. This mosaic dataset can be created inside or outside of a geodatabase.

You can modify the functions for the mosaic dataset by opening the mosaic dataset's Properties dialog box from within the Catalog window.

Cascading mosaic dataset

A mosaic dataset can be used to fuse together multiple sources that include imagery from other servers such as ArcGIS Online, WCS services, image services, or other mosaic datasets. When the mosaic dataset is published as a service, this combination is referred to as cascading. The advantage of cascading is that imagery from one service can be used, for example, as the overview or background to another service and the end users can get a single access point. Cascading enables for example multiple WCS service to be viewed as a single service.

Cascading services can cause bottle necks if not managed correctly. For example, you want to be sure the services are always maintained and live. You also want to avoid fusing too many cascading services together, so you don't have a service containing a service containing another service. You should also avoid creating circular services, so that one service doesn't reference another service that in turn could reference itself. As a general rule a mosaic dataset should not cascade (contain) more than eight other services.

Recommendations for managing imagery collections

You can manage all your imagery in a single mosaic dataset. This is ideal when your data is similar-similar in image type, number of bands, and bit depth. However, when you have large collections of imagery that encompass data from different sources and sensors, it is optimal to organize the imagery into smaller, data-specific collections. It simplifies the setup and maintenance of a mosaic dataset when all the imagery managed within it has a similar source and the same number of bands and bits such as:

These separate source mosaics datasets are easier to manage and then combine them to create the application specific mosaic datasets that are published.

Single orthophoto collection example

You may have a large collection of color aerial imagery, such as thousands of images collected over your state or province. You can create one mosaic dataset to manage all these. This mosaic dataset will likely have 3 bands and be 8-bit. You may want to modify the attribute table to add information specific to the imagery, for example, the acquisition date and the location, such as a county or city. You can then directly publish this or create reference mosaic datasets to provide this imagery to users within your organization. You could modify the boundary of a reference mosaic dataset to only provide the imagery within a particular project area, or you could create one by that only contains the images that meet a particular query, such as a county or city.

Multiple orthophoto collection example

You could then add this imagery to an existing mosaic dataset, such as the color imagery defined above, create a reference mosaic dataset that is used to publish this or create a new derived mosaic dataset that combines the content of this mosaic dataset with imagery from other sources such as Landsat and SPOT. In each case, you can edit the mosaic dataset properties to choose a mosaic method most suitable for your data, such as By Attribute using date or cloud cover.

You may have a collection of aerial photography from three years, such as 1995, 2005, and 2008. These may have different resolutions, such as 1 meter, 2 feet, and 0.5 feet. The earliest collection is panchromatic in a UTM projection and the other two are color in a state plane projection. There are two ways to organize this data: as separate source mosaic datasets and derived mosaic datasets, or as one mosaic dataset. Using source and derived mosaic datasets generally makes the management easier while maintaining optimum performance.

To do this, create three source mosaic datasets. You can specify the band and bit depths when you create them or allow the software to define it when the data is added. In the end there will be a 1-band and two 3-band mosaic datasets. Add your imagery accordingly. You likely don't need to calculate statistics as this data is often color enhanced. Often, creating pyramids for pregenerated tiles generally does not bring any benefits so pyramid generation can be skipped. You may want to build separate overviews so users can view one dataset separately from another at all scales. Modify the attribute table for each by adding the same new field for the Year and populate the field with the year.

Workflow diagram

Next, create one derived mosaic dataset containing three bands. This one will be used to provide the best color imagery combination. Then add the three source mosaic datasets to it using the Table raster type. You shouldn't have to build overviews as they were already created within each source mosaic dataset. You may want to modify some of the properties, such as defining the mosaic method to be By Attribute and specify the default year, such as 3000 to display using the latest imagery. It would be wise to create a reference mosaic dataset to publish the contents of the derived mosaic dataset, if the access will be directly to the dataset. If you do this you will have to define the default mosaic method again, since the mosaic dataset properties are unique for each mosaic dataset. If you're publishing the mosaic dataset as an image service, you can publish it directly. In either case users will have one dataset to access with they can query.

If you manage them as one mosaic dataset then you will not have overviews for each year. This can be problematic or confusing to users who wish to view the mosaicked image for a particular, non-default year. As overviews are created for each year, the overviews for the combined service may not be necessary. If the recent imagery does not overlap the older imagery, then creating overviews may be advantageous to optimize the performance. When you build overviews for this one mosaic dataset consider defining the mosaic method as By Attribute and define the most appropriate year, such as 3000. The same rules apply when publishing this mosaic dataset as the one above.

If new, four-band (blue, green, red, and NIR), orthophotos are obtained in 2010, , you would create a new source mosaic dataset for the 2010 imagery. This would be a 4-band mosaic dataset.

You would then add the 2010 source mosaic dataset to the original 'best color' derived mosaic dataset using the Table raster type. Since this mosaic dataset is designed to support only three bands, only the first three bands would be added. Again overviews may need to be added for optimization, but these would be small since overviews already exist in most areas. By default, users of this mosaic dataset would immediately start seeing the 2010 imagery without needing to change their applications because of the By Attribute mosaic method defined earlier.

To make the false color infrared imagery available you could create a new mosaic dataset (don't specify the number of bands) and add the 2010 source mosaic dataset to it using the Table raster type. Then open the mosaic dataset properties from the Catalog window and add the Extract Bands function. Define the Bands IDs as "4 3 2". Originally the mosaic dataset has four bands-which is the same as the original. However, by adding this function you've defined a default band combination and modified the mosaic dataset to output only three bands.

Additionally, you could create a normalized difference vegetation index (NDVI) mosaic dataset. This could be done by using a reference mosaic dataset to point to the false color mosaic dataset and adding the NDVI function to apply the processing required. Alternatively, a new mosaic dataset could be created that references the 2010 source mosaic dataset and adds an NDVI function.

Satellite imagery collection example

If you have a collection of imagery from similar satellite sensors, such as IKONOS (Orthoready product) or QuickBird (Basic Bundle product) which have four multispectral bands collected at one resolution and a high resolution panchromatic band, you can manage this in a single mosaic dataset. You can create a panchromatically sharpened mosaic dataset from this imagery.

Prior to including the imagery in a mosaic dataset it is advantageous to build pyramids and statistics.

Create a mosaic dataset (the bands and bit depth do not need to be defined). Add the imagery using the IKONOS or QuickBird raster type, making sure the Pansharpen product template is defined in the Raster Type Properties dialog box (this is the default product template). Another benefit to using the appropriate raster type is the footprints for each image will be calculated to exclude unwanted image boarder areas.

Overviews may not be required for this mosaic dataset since it does not define a single cohesive dataset and often other imagery will be used instead at smaller scales. For some workflows it is required to create overviews. It may be advantageous to create the overviews using the By Attribute mosaic method with a base value that will use the latest images or those with the least cloud cover.

A number of attributes will be added as part of the raster type. You can add additional attributes to help manage and organize the data, such as defining the accuracy or quality of the imagery. Similarly you can define an attribute, such as 'Publish', to define if the image is to be published to users. This way you can easily exclude or include specific scenes from publishing or be used for more specific publishing related queries.

You could then add this mosaic dataset as a source to a number of different mosaic datasets. For example you may decide to add some or all the images to the orthophoto mosaic datasets created earlier.

You may have users that wish to access the full image content of the four band satellite mosaic dataset. You could make this mosaic dataset directly available or create a reference mosaic dataset to make it available.

Elevation collection

There are many reasons for creating a mosaic dataset of elevation data, for example, you may want to access all your elevation data from a single source or you may want to use the elevation data as a data source to orthorectify other imagery. In most cases, you can manage all your elevation data in one mosaic dataset. Create a mosaic dataset, specifying the largest bit-depth of your input data, which is generally 32-bit. Then add all your imagery according to its raster type. Be sure that the elevation data represents height as either orthometric or ellipsoidal and that the units of height are the same (such as meters or feet). If they are not then the mosaic dataset requires more steps to create, but you can use the Arithmetic function to modify these values for each input.

See the workflow to convert to or from orthometric and ellipsoidal heights.

See the Units conversion factor table to convert to or from feet, meters, or degrees.

You can then edit the mosaic dataset properties to choose the By Attribute mosaic method and define 0 as the default value; therefore the highest resolution elevation data at the view or requested scale will be displayed or used.

If you have multiple sources for elevation data such as Lidar, bathymetry and sonar, you may consider creating separate source mosaic datasets for these different sources, managing them separately and then creating a single mosaic dataset that combines them.

Generally, users working with elevation data want to use the imagery that is most accurate or has the highest resolution. You can edit the mosaic dataset properties to choose the By Attribute mosaic method. Define LoPS as the order field and 0 as the default value. Therefore the highest resolution elevation data at the view or requested scale will be displayed or used. If a field for accuracy exists, then this could be used instead.

This mosaic dataset can act as a source to multiple referenced mosaic datasets which are created to produce output from the elevation data, such as hillshade, aspect, or slope.

You can see from the above example how with a simple collection of imagery there are choices in how you manage your data. But the main design is to create source mosaic datasets, then bring them together using a derived mosaic dataset, and then publish the data.

To see a workflow for creating a mosaic dataset like the one described above, see Creating a mosaic dataset containing raster data from multiple dates.

Publishing mosaic datasets

Publishing a mosaic dataset refers to making it available to users. This can be done by sharing the geodatabase and giving direct access to the mosaic dataset, or using ArcGIS Server to serve an image service or other service, such as a map service.

If you're planning on sharing a mosaic dataset using direct access, it is recommended that you create a reference mosaic dataset to provide that direct access. Because anyone who can directly access the mosaic dataset can edit it; therefore, you do not want to provide that direct access to your main source or master mosaic dataset.

If you planning on serving the mosaic dataset as an image service you can serve it directly as the users of the image service will not have direct access to the mosaic dataset.

Caching mosaic datasets

You can serve a mosaic dataset in a globe or map document. However, when you do this the user will not have the capability to modify any properties, such as the mosaic method, or be able to query the mosaic dataset. But by using a map service or globe service you can generate a cache which is often the fastest way to provide access to data over the web, or as a local cache to computers or mobile equipment that will get disconnected from the network.

It is advantageous not to include vector and imagery data in an MXD to be published. Generally, it is better that vectors and imagery are served as two separate services that are then mashed together by the client application.

Properties of a published mosaic dataset

When publishing a mosaic dataset as an image service there are many properties that can be modified which control access to the mosaic dataset and individual images. For example, there are settings to

  • Modify the accessible fields in the attribute table
  • Limit the number of images that can be downloaded from
  • Limit the request size
  • Limit the metadata available
  • Define the default mosaic method
  • Define the default compression for transmission

Properties or parameters to consider


data within each image. You can use the Build Footprints tool to modify footprints to exclude parts of images from the mosaic dataset, such as black or white borders or 'secure' areas. Generally footprints are modified in the source mosaic datasets and not modified in referenced mosaic datasets.


This is another way to define values within an image that you don't want included in the output mosaicked image. You can use the Define Mosaic Dataset NoData tool, which inserts the Mask function within the function chain for each image within a mosaic dataset. This can result in slower performance if there are many overlapping images. Generally, it recommended that you modify the footprints on an image to remove data.


By default, the boundary merges all the footprint polygons to create a single boundary representing the extent of the imagery. It can have holes or be a multi-part polygon. This can take time to generate, therefore if you're adding multiple collections of imagery consecutively, using the Add Raster Data To Mosaic Dataset tool, you may want to uncheck the Update Boundary parameter, until you are add your last collection. When you add new imagery to a mosaic dataset you may choose to run the Build Boundary tool to update the boundary, because this tool has an option to append to the existing boundary rather than overwriting it-which can also save time.

The boundary can also be used to exclude and area of imagery in the mosaic dataset. For example, you can import a boundary polygon file that fits your exact area of interest, even if the imagery in the mosaic dataset covers a larger area. You can also edit the boundary using ArcMap's editing tools. If you are adding a service or other larger image to the mosaic to fill in data gaps for you source imagery, you may not want the boundary recalculated to include the full extent of this image. Therefore, you would also uncheck the option to update the boundary.


Generally, if you need to enhance the imagery, then compute the statistics. Statistics are maintained for each image, and for the entire mosaic dataset.

If statistics exists on the mosaic dataset, ArcMap will always apply a stretch by default. If you don't want a stretch applied, you can modify the Is Preprocessed Data setting in the Mosaic Dataset Properties dialog box to equal Yes.


You may need to apply a histogram stretch to your imagery to be sure it displays well. For example, you may need to scale your 12- or 16-bit imagery to display well using 8-bits. You can apply an enhancement to the imagery when you are adding it to the mosaic dataset by modifying the raster type properties. Alternatively, you can add the Stretch function after the imagery has been added.

Color correction

Generally, color correction is only applied to RGB imagery—either a natural or false color imagery product (although it can be done on multiple bands). The recommend workflow is to create a derived mosaic dataset that includes the color imagery and then apply color correction to this. Color correction tools can be accessed using the Color Correction window in ArcMap.

Attribute fields

You can add additional fields to the attribute table to contain all attributes suitable for your source imagery. Some fields are imported from the imagery as defined within the raster type. When you are creating multiple source mosaic datasets that will be merged into a master mosaic dataset you should define consistent fields.

Some common fields you may want to add include:

  1. Start Date—as Date field
  2. End Date—As Date field
  3. Publish—an integer or text field identifying if you want to publish the imagery or not
  4. Accuracy—an integer ranging from 1 to 100
  5. Quality—an integer or text field defining a quality value you define for each image

Also, don't forget to add values to the fields for your overviews. These fields will be accessible for viewing and querying against by the users of the mosaic dataset; therefore, you may want to limit those that are accessible. You can set which fields can be accessed from within the mosaic dataset's Properties dialog box.


Overviews take time to generate; therefore, they should only be created only if they are needed. For example, you will generally compute overviews when creating source mosaic datasets, but you may not need to when creating a derived mosaic dataset. Also, you can use lower resolution images or services as a low cell size data source, thereby removing the need to generate overviews.


If the spatial reference systems of the data and the mosaic dataset or user are based on different spheroids you may need to specify a specific geographic transformation. You can specify the transformation in two location. When adding imagery to the mosaic dataset that has a different datum than the mosaic dataset, set the Geographic Transformation on the Environment Settings dialog box. If you know the user or application will be using a different datum than the source imagery or mosaic dataset, open the mosaic dataset properties (via ArcCatalog or the Catalog window) and click the Defaults tab, then set the Geographic Coordinate System Transformation property.

Example mosaic datasets

The following are examples of some typical mosaic dataset, along with some details for specific properties or considerations:

Color imagery—Best natural color imagery

False color imagery—Best false color (432) imagery

Imagery for interpretation or analysis—For optimum image interpretation of satellite or aerial imagery

Multispectral imagery for analysis—Often, more than 3 bands

NDVI—Normalized difference vegetation index with a color table

Surface or ground elevation orthometric—Best ground elevation, with orthometric (above sea level) heights

Ground elevation ellipsoidal—Best ground elevation, with ellipsoidal height

Slope—Slope in degrees of ground elevation

Aspect—Aspect of ground elevation

HillShade—Hillshade of ground elevation

Shaded Relief—Shaded relief of ground elevation