The ideal way to manage your small to vast collections of image and raster data in ArcGIS Desktop is using a mosaic dataset. A mosaic dataset
- manages your data as a catalog collection, regardless of varying spatial, spectral, temporal, and radiometric resolutions, and with full access to the metadata for each item,
- dynamically delivers a mosaicked image or provides access to the individual images,
- processes the data on the fly, and
- can be shared as a dataset or image service
Mosaic datasets allow users to access a single source to obtain the data they need—simplifying maintenance and application development. The table (catalog) can be accessed allowing users to query the mosaic dataset and access each item stored within it, whereas the mosaicked image can be used like a raster dataset—appears as one continuous dataset and can be processed with the tools used to process a raster.
They can be extremely large both in total file size and number of raster datasets. They do not control the source data, but contain pointers to the source data.
They are created, edited, and managed with the tools in the Mosaic Dataset toolset in the Data Management toolbox.
Data sources
The image and raster data can come from a variety of sources, such as aerial or satellite sensors, scanned maps, the output from analysis, and even lidar data. It can be panchromatic, multispectral, thermal, elevation, or thematic. It can be stored as files on disk or in a 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)).
Image and raster data is added to a mosaic dataset according to its raster type. The raster type simplifies the processing of adding complex image data to a mosaic dataset. It understands file format and specific information about a product such as metadata, as georeferencing, acquisition date, and sensor type, processing, and wavelengths. Raster formats are similar to raster types; however, it only defines how pixels are stored, such as number of rows and columns, number of bands, actual pixel values, and other raster format-specific parameters. In ArcGIS Desktop 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.
By adding raster data according to a raster type, the metadata is read and used to define any processing that needs to be applied. For example, when adding a QuickBird Standard scene the raster type knows the metadata is stored in an .imd file and the bands are organized into one or more .tif files. It also knows that this imagery could be pan-sharpened and orthorectified, so depending on the options you choose, it will add the appropriate functions so the image can be processed accordingly. 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 orthorectification would be missing.
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.
Processing through functions
Functions are a key component to every mosaic dataset. They enhance or modify the mosaicked image product by applying on-the-fly processing operations such as orthorectification, image enhancements, and image algebra. You can add functions to the mosaic dataset or to individual rasters within the mosaic dataset, or they may be added when the data is added to the mosaic dataset via the raster type. For example, when specific raster data products (such as from a satellite sensor) are added to a mosaic dataset, some functions are automatically added to the raster data.
Functions allow multiple products to be created from a single raster source because imagery is processed as it's accessed.
Some of the functions applied to the mosaic dataset or the items within it include
- Stretch—This enhances the image by adjusting the brightness, contrast, and gamma based on the statistics of the data.
- Composite Band—This combines multiple files into a single image, such as multiple TIFF files representing individual bands in a single scene.
- Arithmetic—This performs an arithmetic operation between two spatially overlapping rasters or a raster and a constant value.
- Hillshade—This allows you to generate a hillshaded image from an elevation model.
- LAS To Raster—This allows you to visualize LAS files in a mosaic dataset by determining how they will be rasterized.
Properties
The properties of a mosaic dataset include the general properties, which are similar to those you find for all raster datasets, such as the data source, extent, cell sizes, and bit depth. But they also include more advanced information that affect how the mosaicked image will be presented to the user (or client) and how they might interact with it, and can also impact the performance of the server or image service if the mosaic dataset is published.
For example, there are several mosaic methods that define the order of the rasters that are mosaicked together to create the mosaicked image, such as the By Attribute mosaic method, which orders the imagery based on an attribute such as a date, the North-West mosaic method which makes sure the image with its center in the north-west corner of the display is ordered on top, or the Lock Raster mosaic method which displays only the selected images. There is a property that controls if the images will be clipped by their footprints and another to control if the mosaic dataset is clipped by its boundary. The Allowed Fields property defines which fields in the attribute table will be visible when the mosaic dataset is served as an image service.
When publishing a mosaic dataset using ArcGIS Server, the server administrator can modify some of the properties as part of the settings on the image service; however, they will not be able to exceed the maximums you have set. For example, if you limit the allowed mosaic methods to only three of the methods, the administrator will not be able to add a fourth method. Or, if you set the maximum number of downloadable items, they can reduce the number but not increase this number. If you change the properties to exceed or limit a value, such as the Maximum Size Of Requests, you need to completely republish the mosaic dataset. If you restart the image service, the changed properties in the mosaic dataset will not be picked up.
Some properties even control how the data is added to the mosaic dataset and impacts how ArcGIS Desktop will render the dataset. For example, the product definition allows you to customize the mosaic dataset to contain data with a specific number of bands and wavelengths. It controls how the data is added to the mosaic dataset, how it displays by default, and aids in some processing. The product definition is typically used to support specific satellite imagery products, such as QuickBird and Landsat, but you can customize it by defining the number of bands, the band order, and the wavelength ranges supported by each band.
The most commonly used product definitions are:
- Natural Color (RGB)—This creates a three-band mosaic dataset, with red, green, and blue wavelength ranges.
- Natural Color (RGBI)—This creates a four-band mosaic dataset, with red, green, blue and near infrared wavelength ranges.
- False Color (IRG)—This creates a three-band mosaic dataset, with near infrared, red, and green wavelength ranges.
Using a product definition helps when adding data containing wavelength information in its metadata. If the wavelength information is ordered differently in the inputs, they will all be ordered correctly when added to the mosaic dataset. For example, if band 1 in a QuickBird scene is a blue wavelength, and band 3 in the mosaic dataset is designed to contain the blue wavelengths, then the QuickBird's blue band will be mapped to the mosaic dataset's blue band. Without this, the QuickBird's blue band may be mapped to the mosaic dataset's red band.
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.
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 accidentally make modifications to your mosaic dataset such as adding or removing data.
Different mosaic datasets may be published to define different types of data, such as natural color imagery, false color imagery, or elevation. Data does not have to be organized by a specific geography, type of sensor, or date range since a mosaic dataset can accommodate all of these differences.
Typical examples mosaic datasets include:
- Natural color imagery—The imagery matches the colors that we see with out eyes (often using a 3,2,1 (RGB) band combination)
- False color imagery—Containing false color infrared imagery (often using a 4,3,2 (RGB) band combination)
- Multispectral imagery for visual interpretation—Imagery that has been enhanced for visual interpretation and may be pan-sharpened
- Multispectral imagery for analysis—Imagery with all available bands providing radiance or reflectance values
- NDVI—A colorized normalized difference vegetation index
- Elevation—A compilation of DEMs representing orthometric (above sea level) heights or ellipsoidal heights
- Slope—An image representing slope calculated in degrees from elevation
- Aspect—A colorized image representing aspect calculated from elevation
- HillShade—Hillshade image created from elevation
- Shaded Relief—Shaded relief image created from elevation
- Scanned topographic or application-specific maps
- Analysis results producing a thematic image with pixel attribute values
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.
It is generally advantageous to separate mosaic datasets into two types: those that are primarily used for management and those that 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. The source and derived mosaic datasets discussed below are symbolic names used to help convey an understanding of the organizational structure of mosaic datasets, whereas a reference mosaic dataset is a physically different form of a mosaic dataset.
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. Next 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 the same number of bands and bit depth. These source mosaics are managed and used to refine aspects of the collection: 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 bands; therefore, you should specify these values or define a product definition to control the output of the mosaic dataset. 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.
It is recommended that you add a source mosaic dataset to another mosaic dataset using the Table raster type. Using the Table raster type will create a mosaic dataset containing all or a selection of the table items in the source mosaic datasets, not just a single item representing the source mosaic dataset. This will give you the ability to perform queries and continue to access the individual metadata for each item. You can also add functions to individual items and more easily customize the mosaic dataset with seamlines, mosaic methods, and color corrections. Additionally, the Synchronize Mosaic Dataset tool with the Update cell size ranges option disabled 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. This will thereby limit your ability to perform queries, limit metadata access to the source mosaic dataset instead of each image, and it will limit the scalability of the mosaic dataset.
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. Instead of building overviews, you could use another image or image service to provide imagery coverage for the full extent of the mosaic dataset. When adding this image you should uncheck the option to build the boundary because 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. 
Any properties set on the input mosaic dataset will be carried over to the reference mosaic dataset—such as the default mosaic method—or mosaic dataset functions. You can modify or remove any of these without affecting the input mosaic dataset. You can modify the functions or properties by opening the mosaic dataset's Properties dialog box from within the Catalog window.
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:
- Preprocessed ortho image tiles of the same date
- Imagery collected from a similar sensor with the same number of bands and bit depth
- Collections of 4 band 16-bit Imagery (QuickBird, IKONOS)
- RapidEye (5 band)
- SPOT
- Landsat 5 or 7
- ASTER
- Imagery from a single aerial survey project
- Elevation data from one source
- SRTM
- Lidar
These separate source mosaics datasets are easier to manage. You can 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 that only contains the images that meet a particular query, such as a county or city.
Multiple orthophoto collection example
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 one combined derived mosaic dataset, or as one mosaic dataset. Using a combination of 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 since this
data is often color enhanced. Creating pyramids for
pregenerated tiles generally does not bring any benefits, so pyramid
generation can be skipped. But you should build overviews on each of the source mosaic datasets. This way, when users query the derived mosaic dataset by date as they zoom in and out the image will be consistent. If there aren't overviews for each, then you would have to build overviews on the derived mosaic dataset, and this can only be generated for a single attribute date. Modify the attribute table for each by adding the same new
field for the Year and populate the field with the year.
- 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 and the Update cell size ranges option disabled. You won'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're publishing the mosaic dataset as an image service, you can publish it directly. In either case, users will have one dataset to access, query, and explore.
Managing with one mosaic dataset
One reason not to manage this data using a single mosaic dataset is because there will not be overviews for each year. Overviews are generated from the default mosaic method; therefore, only the default (newest) imagery can be used to create the overviews. This can be problematic or confusing to users who wish to view the mosaicked image for a particular, nondefault year. However, if you choose to use only one mosaic dataset, then build the overviews using the By Attribute mosaic method and define the most appropriate year, such as 3000. The same rules apply when publishing this mosaic dataset as the one above.
Adding new data
New imagery is inevitable, and a lot of newly acquired aerial imagery has four bands (blue, green, red, and NIR). If you obtained new orthophotos 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 derived mosaic dataset using the Table raster type and build overviews. 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 to the derived mosaic dataset 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.
Additional mosaic datasets
To make the false color infrared imagery available you could create a reference mosaic dataset from the source four-band mosaic dataset. 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.
You could also 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.
Starting at ArcGIS 10.1 for Server, you now have the ability to use server-side processing when you share your image services. This allows you to create one image service from your mosaic dataset, which can use many different server-side functions to process and display your data.
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.
When working with satellite imagery, there is information—such as wavelengths and sun angles—that can be useful. To ensure this information is used, it is best to define a product definition when creating the mosaic dataset. This product definition defines the wavelength ranges that the mosaic dataset will support for each band. Since the QuickBird and IKONOS imagery have similar wavelength ranges, then either one can be chosen as the product definition.
For this scenario, create your mosaic dataset using the IKONOS product definition. Add the imagery using the IKONOS or QuickBird raster types, depending on which source you're adding. Make sure the Pansharpen product template is defined in the Raster Type Properties dialog box (this will be the default product template). Another benefit to using the appropriate raster type is that the footprints for each image will be calculated to exclude unwanted image boarder areas.
Overviews may not be required but 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 determine if the image is to be published to users. This way you can easily exclude or include specific scenes from publishing, or they can 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 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, 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, bring them together using a derived mosaic dataset, 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.
Sharing mosaic datasets
You can share a mosaic dataset with users by sharing the geodatabase and giving direct access to the mosaic dataset, or using ArcGIS Server to publish an image 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. 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 mosaic dataset.
If you're 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 cache an image service or cache a map service or globe service containing raster data or an image service. Generally, the pyramids generated for raster datasets or the overviews generated for mosaic datasets result in image data being served at an acceptable rate. However, if you know that a particular image or area of interest will be repeatedly visited, you may want to generate a cache.
It is advantageous not to include vector and imagery data in the map or globe document 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
- Limit the request size
- Limit the metadata available
- Define the default mosaic method
- Define the default compression for transmission
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. They can also impact the number of mosaic dataset overviews that are generated. Generally, you should build pyramids for images with greater than 3,000 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 enhanced for display. 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 1,000 and use the quotient (integer) as the skip factor.
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 lossless (for example, LZW) or lossy (for example, JPEG) compression. You could choose to use a wavelet-based compression, 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).
Properties or parameters to consider
Footprints
The footprints define the extent of each image within the mosaic dataset. 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.
NoData
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 is recommended that you modify the footprints on an image to remove data.
Boundary
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 multipart polygon. This can take time to generate; therefore, if you're adding multiple collections of imagery consecutively, using the Add Raster Data To Mosaic Datasettool, you may want to uncheck the Update Boundary parameter, until you 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 an 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 your 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.
Statistics
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 a property to turn off this default: open the mosaic dataset's properties, click the General tab, then set the Source Type property value to Processed.
Enhancements
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 recommended 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:
- Start_Date—As a Date field
- End_Date—As a Date field
- Quality—An integer or text field defining a quality value you define for each image
- Comments—A text field with any additional comments
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
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.
Datums
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 locations. 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
- Create a 3-band, 8-bit mosaic dataset
- May also include both color and panchromatic imagery
- Default mosaic method is By Attribute, to display with the latest and best quality on top
- Default compression is JPEG with 80 percent quality
- If imagery requires color correction then add color correction
False color imagery—Best false color (432) imagery
- Create a 3-band, 8-bit mosaic dataset
- Add all the bands and then apply the Extract Band function to define the 432 combination, or define the band combination when adding the imagery to the mosaic dataset
- Default mosaic method is By Attribute, to display with the latest and best quality on top
- Default compression is JPEG with 80 percent quality
- May apply color correction to remove trends
Imagery for interpretation or analysis—For optimum image interpretation of satellite or aerial imagery
- Create a 4-band, 16-bit mosaic dataset
- Default mosaic method is By Attribute, to display with the latest and best quality on top
- Default compression is JPEG with 90 percent quality
Multispectral imagery for analysis—Often, more than 3 bands
- Create a mosaic dataset specifying the number of bits and bands equal to maximum of the imagery
- Default mosaic method should display the latest or best quality on top
- Generally created for analysis, therefore, the default compression should be LZW
- Typically when referencing source mosaic datasets, this should exclude the pan-sharpened imagery and redefine the MinPS of the multispectral imagery to equal 0. This ensures that pan-sharpened imagery is not used for analysis.
NDVI—Normalized difference vegetation index with a color table
- Create a 3-band, 8-bit mosaic dataset
- Default mosaic method should display the latest or best quality on top
- Default compression is JPEG with 90 percent quality
- Create as a derived mosaic dataset from the Multispectral imagery for analysis mosaic dataset
- Add the NDVI function to the mosaic dataset
Surface or ground elevation orthometric—Best ground elevation, with orthometric (above sea level) heights
- Create a 1-band, 32-bit mosaic dataset
- To display the most accurate on top, create a field in the attribute table to identify this value and set the default mosaic method to By Attribute
- Default compression should be LZW
- Could also include a low-resolution DEM or service as a background source for areas missing elevation data
Ground elevation ellipsoidal—Best ground elevation, with ellipsoidal height
- Properties the same as ground elevation orthometric mosaic dataset
- Most elevation data is orthometric. There are some requirements for accurate ellipsoidal service (for example, for accurate orthorectification of satellite imagery). These mosaic datasets can be created by applying an accurate geoid to the orthometric mosaic dataset. See, Converting from orthometric to ellipsoidal heights.
Slope—Slope in degrees of ground elevation
- Create a 1-band, 8-bit derived mosaic dataset, based on the ground elevation orthometric mosaic dataset
- Default mosaic method should display the best quality on top
- Default compression should be LZW
- Add the Slope function to the mosaic dataset
- This would be quantized to an accuracy of 1 degree
- For some applications, a mosaic defined using a float pixel type would be better
Aspect—Aspect of ground elevation
- Create a 3-band, 8-bit derived mosaic dataset, based on the ground elevation orthometric mosaic dataset
- Default mosaic method should display the best quality on top
- Default compression should be LZW
- Add the Aspect function to the mosaic dataset
HillShade—Hillshade of ground elevation
- Create a 1-band, 8-bit derived mosaic dataset, based on the ground elevation orthometric mosaic dataset
- Default mosaic method should display the best quality on top
- Default compression is JPEG with 80 percent quality
- Add the Hillshade function to the mosaic dataset
Shaded Relief—Shaded relief of ground elevation
- Create a 3-band, 8-bit derived mosaic dataset, based on the ground elevation orthometric mosaic dataset
- Default mosaic method should display the best quality on top
- Default compression is JPEG with 80 percent quality
- Add the Shaded Relief function to the mosaic dataset