The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
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Finally, the information variability from an image to another is 
a very tricky problem for comparing their content: same objects 
can produce very different signals depending on capture 
parameters and external conditions. For example, different 
wavelengths or polarizations provide different results but it is 
also the case when using the same Radar parameters and when 
external conditions are not the same (after raining, the dielectric 
constant of the ground changes and produces a different 
response). 
Another main difficulty with Radar image interpretation 
concerns the speckle, which is a noise that gives the images a 
grainy appearance (Rees, 2001). The speckle is a direct 
consequence of the wave coherence: the interaction with the 
target may shift the wave phase because of the target height 
variations and/or physical properties. The result of this 
interaction is a multiplicative noise. This effect has been 
modeled through a statistical approach (Chitroub et al., 2002) 
although it is deterministic. • 
Many algorithms have been proposed to reduce this noise, 
especially through the creation of filters (Touzi, 2002) that are 
supposed to widely eliminate the speckle effect while 
preserving most of the information being in the images 
(radiometry, contours, texture,) and not generating any artifact. 
Specific algorithms have to be designed for extracting 
primitives (or landmarks) from images because of speckle. For 
example, when using classical tools such as gradient or 
Laplacian filters, we obtain a variable rate of false alarms (it 
increases in areas where the signal is very intense). Thus, the 
main objective when designing a new algorithm for extracting 
primitives from Radar images is to create a filter that produces a 
constant rate of false alarms; these filters are called CFAR 
detectors, CFAR meaning “Constant False Alarm Rate” (Touzi 
et al., 1988). 
2.2.4 Conclusion on Spaceborne platform Radar images: 
Automatic registration of Spaceborne platform Radar images is 
still a challenge because of some key difficulties: 
We do not have any accurate information on the 
image relative positions 
These images went through various geometrical 
deformations 
Radiometry may be modified by speckle but also by 
several physical phenomena related to the 
backscattered signal. 
Concerning this last point (i.e. radiometry modifications): 
Speckle: this noise changes the value of the similarity 
measure and thus, it reduces the efficiency of 
landmark extraction 
Physical phenomena: a main question when the two 
images come from very different acquisition 
conditions is to determine if there is enough common 
information to provide an automatic registration. 
c) Spaceborne platform Radar image registration 
As we discussed in the previous section, registering such Radar 
images is still a challenge and thus, many research groups focus 
their work on this topic (Wessel et al., 2007). In this section, we 
mainly discuss on the matching process itself and on the way 
the Radar image generation has an effect on this process. 
We remind that we can use either an “area-based” approach or a 
“feature-based” approach, the first one using statistical 
properties of the pixel neighborhood, and the second one using 
primitives extracted from images as landmarks to be paired. 
Now, let us see how the Radar imaging specificity makes 
difficult such processes. 
Neighborhood statistical properties are characteristic of what is 
called texture, and thus, “area-based” approaches can be seen as 
texture pairing methods. In fact, two textures are superimposed 
in Radar images: the texture that is characteristic of the area (we 
call it the “scene” texture) and the speckle texture. The main 
difficulty is then to find algorithms that describe correlations 
between the “scene” textures of the two images without having 
any interaction with the speckle texture although these two 
textures (“scene” and speckle) are mixed in the image. 
Primitive extraction from SAR images is also dependent on 
speckle, but in a different way than for “area-based” approaches. 
As we have seen before, the SNR (Signal to Noise Ratio) is not 
very high and thus there are a lot of false alarms (with a variable 
rate): these false alarms result in small variations of the 
primitives used as landmarks, which produces a slight 
instability or a loss of accuracy. 
A question arising when registering two SAR images acquired 
with different conditions during data acquisition or with 
different sensor features, is the nature of the information they 
provide. As already mentioned before, information strongly 
depends on these factors; two SAR images gather a common 
part of information, while at the same time they carry another 
part of unique information due to specific internal and/or 
external parameters of acquisition. Matching step in a 
registration algorithm is thus a difficult problem in SAR 
imagery, as methods should be able to rely on common 
information only. Scene texture is dependent on acquisition 
parameters in a very sensitive way, so that area-based methods 
may be particularly affected. Feature-based are concerned too: 
landmarks may simply not occur simultaneously in two SAR 
images for example 
Finally, local geometrical distortions provide an additional level 
of difficulty in the registration problem. These distortions result 
in data distribution variations, which has a disturbing effect 
when the viewing geometry on a given area is very different 
from one image to the other one. 
As a conclusion, we can say that classical registration 
approaches, whatever they are (area or feature based) have to 
face to very tricky problems in SAR imaging because of noise, 
distortion and complementary information. 
In the next section, we introduce an alternative solution to the 
SAR image registration problem that uses the Hough Transform. 
Although it is not classically used for solving such kind of 
problems, the Hough Transform brings a help to automatically 
discriminate the common information from the complementary 
one, and thus to have a common support for registering SAR 
images that have been acquired using different parameters. 
2.4 The Hough Transform 
P. Hough introduced his transformation in the early 60s (in 
1959) and then, as a US patent, in 1962 (Hough, 1962); in 1972, 
R. Duda and P. Hart (Duda and Hart, 1972) showed this 
transformation being efficient for the detection of lines and 
curves in pictures; then, the Hough Transform has been 
generalized to provide the detection of parameterized models 
such as transformations, and not only geometrical primitives.