Post on 06-Apr-2018
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Title : Automatic method used for geometric
correction for SAR
Abstract
Radar imagery has become one of the mostimportant data sources and efficient tools for
terrain analysisand natural resource surveys
since 1960s. With the development of
technology in the field of radar remote
sensing, new generation of radar sensors, i.e.,
Synthetic Aperture Radar (SAR) was born.
Unique specifications of radar systems and
images versus optical ones led to a whole new
series of applications for radar imageries all
over the world. However, the level ofachievable accuracy from radar imageries is
still a problem for their applications.
Multiplicative noise such as speckle which is
unavoidable part of coherent radar images,
degrade radiometric quality and
interpretability. Moreover, geometric
distortions such as foreshortening, layover,
shadow and other problems related to special
imaging geometry of radar systems, decrease
reliability of radar imageries. Thus,
radiometric and geometric corrections and
calibrations must be applied to the radar
images before using them.
Introduction
Radar remote sensing, like optical remote
sensing, is used to produce the image of
Earths surface. A radar image is a record of
the interaction of energy and objects at the
Earths surface. Its appearance is dependent
on variables such as geometric shape, surface
roughness and moisture content of the target
object, as well as the sensor-target geometry
and the transmission direction (look direction)
of the radar sensor. There are significant
differences, however, between how a radar
image is formed and what is represented in
that image compared to optical remote
sensing imagery [3]. In compare to optical
remote sensing, radar imaging has some
advantages. First, as an active system, it is a
day/night data acquisition system. Second,
considering the behavior of electromagnetic
waves in the range of RADAR wavelength, it
can be seen that atmospheric characteristics
such as cloud, light rain, haze, and smoke has
little effect on the capability of RADAR data
acquisition system. This makes RADAR as an
allweather remote sensing system. Last but
not least, as the RADAR signals partially
penetrate into soil and vegetation canopy, in
addition to surface information, it can provide
subsurface information too. The returned
signal (backscatter) from ground objects(targets) is primarily influenced by the
characteristics of the radar signal, the
geometry of the radar relative to the Earths
surface, the local geometry between the radar
signal and its target, and the characteristics of
the target.
Content
Radar systems are side-looking distance
measuring systems, thus key geometric
parameters are the incident angle, local
incident angle and look direction. The side-
looking geometry of radar results in several
geometric distortions, such as slant range
scale distortions and relief distortions.
Geometric corrections include slant to ground
range, registration, and local incident angle
corrections (if topographic information is
available). Generally speaking, geometriccorrection algorithms are classified into three
methods:
Slant to ground method Polynomial method (best fit
approximations)
Radargrammetric method (knownsensor geometry)
Ground Control Points (GCPs) are
used to establish and/or refine thetransformation.
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Slant/Ground Range Conversion
SAR data are acquired in slant range. Slant to
ground range conversion is used to project theacquired image to the ground system. To do
this, one needs to know (or assume) imaging
geometry, platform altitude, range delay and
terrain elevation. Resampling is used to give
uniform pixel spacing (in ground range) across
the image swath. Slant to ground range
conversion can be done during signal
processing or during image processing.
Generally, it is applied after radiometric
correction. Approaches and algorithms usedare a function of analysis objectives.
RADARSAT ground range products assume a
sea level ellipsoid earth model with zero relief.
Image Registration Polynomial Transforms
Polynomial transform uses a best-fit model.
First order polynomial is a shift-rotation of the
image, whereas the third order polynomial is a
complex warping of the image. Second orderpolynomials are used for images requiring
nonlinear warping. Third and higher order
polynomials create a more complex image
transformation. Higher order transforms
require a greater number of ground control
points (GCPs) in order to produce the
transform model. High order does not
guarantee higher accuracy. Higher order
usually ties the image down at the GCPs, but
can increase errors between the GCPs.
Radargrammetric Method
Geocoding is the geometric correction of
image data to a map projection. Traditional
method of geocoding is the polynomial
transform. This method does neither model
the viewing geometry nor use elevation data
to correct for topography. The most accurate
geocoding method is the radargrammetric
method. The radargrammetric process
consists of three steps as following:
Ephemeris modeling and refinement (if GCPs
are provided)
Sparse mapping grid generation
Output formation (including terrain
corrections)
Radargrammetric method uses analytical
formulation of the distortions during image
formation. Therefore, the geometric
correction is done using the platform
(ephemeris and ancillary data), sensor
(integration time, pulse length, depression
angle), and DEM information. Output of
radargrammetry is an Ortho-image
corrected for all distortions, including relief.
The planimetric accuracy of the final ortho-
image is dependent on the accuracy of GCPs
and the DEM.
The advantages of radargrammetric
method are as following:
Unified projection system.
Direct image to terrain correction.
Only one resampling of an image (slant
range to map projection is directly done,
no intermediate conversion to ground is
required).
Homogeneity in the ortho - image
generation.
Use of a DEM or a mean altitude.
Better integration with GIS or digital
maps.
Comprehension and control of the full
geometric process and of the resulting
errors.
Conclusion
Geometric corrections in radar imageries are
different than optical ones as the geometry of
the imageries are different. The
radargrammetric method has a betterperformance in compare to the other two
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methods. The reason seems to be obvious as
radargrammetry considers geometry of
imaging, uses both orbital parameters of the
sensor, and DEM of the region.