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
ARCGIS DATA MODELS FOR MANAGING AND PROCESING IMAGERY
Hong Xu, Peter Becker
Environment System Research Institute (Esri)
Redlands, California, USA
pbecker@esri.com
KEY WORDS: Imagery, processing, GIS, mosaic, cataloging
ABSTRACT:
As remote sensing technology advances, imagery becomes one of the most important data sources for GIS applications—as it can quickly
provide the most current and most detailed information with high resolution image data. Traditional image processing workflow
becomes inefficient in GIS applications especially in the case of disaster analysis. This paper introduces two ArcGIS data models that are
designed for on-demand image processing in managing imagery: raster product and mosaic dataset. A raster product allows you to access
single scene remote sensing image products dynamically such as a pan-sharpened and orthorectified GeoEye image or multi-spectral
Landsat scene with a single drag and drop to the map display. Mosaic datasets allow you to catalog a large collection of images from
many sensor platforms, process them on-the-fly and dynamically mosaic the images together. The key concept—the raster function that
is used to implement on-the-fly image processing capabilities—will also be discussed.
1. INTRODUCTION
As one of the most important data sources for Geographic
Information Systems (GIS), imagery has been widely used in
many GIS applications such as mapping, urban planning,
environmental research, disaster analysis, and so on. In the past,
remote sensing images had to be preprocessed using remote
sensing or photogrammetric software packages before their use in
GIS applications. This — process normally involved
orthorectification, pan-sharpening, image enhancing, mosaicking,
clipping, etc. Each process generated many intermediate results
which took up a lot of disk space, and the space multiplied if
multiple products were required. Also, the time taken to write
intermediate results to the disk prolonged the whole processing
time, especially for GIS systems or projects that involved large
amount of image data. The traditional workflow became
inefficient in updating GIS systems and could not meet the
requirement of the rapid response system for disaster analysis. As
image volumes and resolutions increase, GIS systems must be
able to process remote sensing images faster, manage images
more efficiently, and deliver the final products to users in a timely
fashion. To meet these requirements, recently released ArcGIS
software packages have introduced two data models—the raster
product and mosaic dataset—that are used for on-the-fly access of
remote sensing image products, from either a single image scene
or a mosaic of multiple image scenes. These provide a solution
for GIS users that need to quickly access and use remote sensing
images in their GIS applications.
2. ARCGIS DATA MODELS
ArcGIS software can read not only the processed images, e.g.
georeferenced and orthorectified images stored in any image
format, but also the preprocessed images from various sensor
platforms. The preprocessed imagery contains image data and the
associated metadata such as band wavelength, sensor camera
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model, sun angle, sun azimuth, cloud coverage and other
information which will be used in processing the image data and
producing image products that can be used in GIS applications.
These include multiple-band natural color products (RGB), false
color infrared products (RGBI), pan-sharpened products, and so
on. The raster product model allows users to access these products
from a single image scene with an easy drag and drop operation,
while the mosaic dataset model allows the user to access a mosaic
of these products from multiple image scenes by performing on-
the-fly image processing and dynamic mosaicking. Furthermore,
the mosaic dataset model provides image cataloging capabilities
which allow image searching and discovery through the web. The
on-the-fly processing capabilities of the two models are based on
a concept called raster function which defines the process chain
that will be applied to process the image data.
2.1 Raster Functions
A raster function is a pixel based mathematical model that defines
an image processing operation. A raster function contains
arguments which include the input image and other parameters
required by the function. For example, a hillshade function
contains a reference to a digital elevation model (DEM) file, an
azimuth parameter, a latitude parameter, and a z factor parameter.
When the image is accessed, the rendered pixels in the display
will be hillshaded. ArcGIS supports many raster functions,
including functions that perform geometric processing
(polynomial transformation, RPC transformation, and frame
camera transforms), radiometric processing (various stretch
algorithms, pan-sharpening, and filtering), image processing
(clipping and mosaicking), and terrain processing (hillshade,
slope, and aspect). Furthermore, ArcGIS software provides
capabilities that allow users to augment existing processing
capabilities by adding a custom raster function if it is not
supported out of the box.
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