> XXXIX-B4, 2012
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virtual mosaic view of the
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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B4, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
Figure 8. Geoprocessing model creating a virtual mosaic, a mosaicked
TIFF, and image tiles
2) Cataloging a large collection of images: As the mosaic dataset
manages the footprints in geodatabase but references the image
data on disk, it becomes an advanced model for managing large
image collections. First, a mosaic dataset of a large image
collection can be created quickly as it does not load pixels into the
geodatabase. Second, by taking advantage of the geodatabase
technology, there is virtually no limit on the number of images
that can be managed by a mosaic dataset. The limit is only in the
hardware resources. Third, the mosaic dataset can manage data of
various sensor platforms. Figure 9, shows the steps to create a
mosaic dataset from Landsat and QuickBird images.Last, since
the mosaic dataset footprint table contains information about the
image, such as location, sensor type, image acquisition date, cloud
cover, and so on, you can search and retrieve images from the
mosaic dataset using attribute and location queries. A typical
example of the search can be “all images acquired after the year
2000 with cloud cover less than 3% and within my study area
delineated by a polygon”.
Figure 9. Geoprocessing model creating a mosaic dataset for
cataloging and searching
3) Serving an image mosaic and catalog for internet access: Using
the ArcGIS Server technology, the mosaic dataset can be served
as an image service to provide dynamic mosaicking and
cataloging capabilities. Figure 10, shows the search results from a
Landsat service of the world using “AcquisitionDate > date
‘2008-01 -01’” plus a polygon around the Great Lakes area in
North America. Furthermore, once selected, users can directly
use the services with only the specified images being processed,
export the orthorectified images in a well-known image format, or
download the original image with metadata information—straight
from the server to the desktop machine or browser. Users can also
export the mosaic to a well-known image format with a user
expected projection.
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Figure 10. Search result from an image service
served by a mosaic dataset
2.3.4 Reference mosaic datasets
Because of the nature of on-demand processing, multiple mosaic
datasets can be created from the same source data by simply
referencing the source mosaic dataset and adding raster functions.
For example (Figure 11), from a multiple band mosaic dataset of
Landsat image data, we can create a false color mosaic dataset by
simply creating a reference mosaic dataset and adding a function
to extract band , or create an NDVI mosaic dataset by adding a
NDVI function.
Figure 11. Mosaic dataset of RGB, False Color,
and NDVI that reference the same source data
2.3.5 Mosaic dataset updating
Mosaic datasets provide an automatic updating mechanism. New
images in the data folder can be automatically added to the
mosaic dataset using the same raster type used in the mosaic
dataset through a synchronize operation. Existing images in the
mosaic dataset can also be updated if the sources or image
properties are changed.
2.3.6 Mosaic dataset cascading
You can create a master mosaic dataset from one or multiple child
mosaic datasets by using a special raster type called Table. This
raster type will copy all the records and the raster type
information from the child mosaic dataset to the master mosaic
data. Any updates to the child mosaic datasets can be
synchronized to the master mosaic dataset. This framework
enables one to manage data in an organization that has multiple
departments or teams. In the example showed in Figure 12,
multiple counties create their own mosaic datasets to manage their
Git image files
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Figure 12. Cascaded mosaic dataset
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