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LOSSLESS COMPRESSION OF SYNTHETIC APERTURE RADAR
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LCDR R.W. Ives, USN , N. Magotra , and G.D. Mandyam m .
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Sandia National Laboratories, Albuquerque, New Mexico, USA 87185
Department of Electrical and Computer Engineering, University of New Mexico
Albuquerque, New Mexico, USA 87131
Synthetic Aperture Radar (SAR) has been
proven an effective sensor in a wide variety of
applications. Many of these uses require
transmission and/or processing of the image data
in a lossless manner. With the current state of
SAR technology, the amount of data contained in
a single image may be massive, whether the
application requires the entire complex image or
magnitude data only. In either case, some type of
compression may be required to losslessly
transmit this data in a given bandwidth or store it
in a reasonable volume. This paper provides the
results of applying several lossless compression
schemes to SAR imagery.
Synthetic Aperture Radar (SAR) is useful
in many applications, including oil slick
monitoring, terrain mapping and navigation, and
automatic target recognition (ATR). Recent
advances in technology have resulted in data
collections over larger areas and at higher
resolutions. A direct result of this is the massive
increase in the amounts of data being collected. It
is important to find ways in which the size of this
data can be reduced for storage and transmission
purposes. There already exist many readily
available, effective techniques for image
compression (e.g. JPEG). However, most of these
methods are lossy; in the area of SAR imagery,
some applications cannot tolerate any
compression losses, even though the presence of
clutter in an image may give the impression of
noisy data. Prime examples of this type of
application are SAR interferometry (e.g., 3-D
terrain mapping) or simply reducing the downlink
data rate from a satellite: this data is used in a
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variety of research applications, making it
impossible to predefine what an acceptable level
of information loss would be . Therefore,
there is significant interest in the area of lossless
SAR image compression.
Most lossless compression techniques
follow two stages, the first being a "decorrelator"
and the second being an entropy coder . In
many kinds of two-dimensional data, high
correlation exists between neighboring data
points, i.e. pixels; the decorrelator serves to
produce uncorrelated data points, whose collective
entropy should be less than the original data.
This is usually accomplished by determining
predictions of incoming data points based on past
data, and forming as output "error residuals", i.e.
differences between predicted and actual values.
The entropy coder will then take advantage of this
reduction in entropy and will perform a fully-
reversible compression of the output of the
The data used for this research is one
meter resolution SAR images collected by Sandia
National Laboratory's Airborne Multisensor Pod
System (AMPS) of the region near Hanford,
Washington. Lossless compression techniques
were applied to both real (detected/magnitude
only) and complex (in-phase and quadrature)
data. Both types of data were processed as some
applications use only magnitude data (e.g. many
ATR systems), while others require complex data.
2. METHODS FOR DECORRELATION
Many methods exist for decorrelation;
most of these belong to traditional prediction
methods. A few of these methods will be
analyzed with respect to SAR imagery. One such
method is the recursive least-squares lattice
(RLSL) filter , which is widely used in adaptive
filtering. Rather than using the RLSL for a
traditional application such as channel
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Ives, R.W.; Magotra, N. & Mandyam, G.D. Lossless compression of synthetic aperture radar images, article, February 1, 1996; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc666534/m1/1/: accessed November 20, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.