8/2/2019 ASSMENT10

1/3

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.

8/2/2019 ASSMENT10

2/3

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

8/2/2019 ASSMENT10

3/3

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.