Advances in Remote Sensing, 2013, 2, 242-246 Published Online September 2013 (
Application of Multi-Frequency SAR Images
for Knowled g e Acquisition
V. Battsengel1, D. Amarsaikhan2, A. Munkh-Erdene2, Ch. Bolorchuluun1, Ch. Narantsetseg1
1School of Geography and Geology, National University of Mongolia, Ulaanbaatar, Mongolia
2Institute of Informatics, Mongolian Academy of Sciences, Ulaanbaatar, Mongolia
Received May 8, 2013; revised June 8, 2013; accepted July 8, 2013
Copyright © 2013 V. Battsengel et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The aim of this research is to apply TerraSAR X-band, Envisat C-band and Palsar L-band synthetic aperture radar (SAR)
images for a knowledge acquisitio n process. For the stud y, backscattering proper ties of different natural and man- made
objects of urban environment are analyzed on the basis of statistics of signatures of the selected classes. After the
knowledge acquisition, for the acquired knowledge representation, a rule-based approach is proposed. Overall, the re-
search demonstrated that the multi-frequency radar images can be effectively used for the knowledge acquisition as well
as for the analysis of different land cover types.
Keywords: Knowledge Acquisition; SAR; Microwave Wavelengths; Backscattering
1. Introduction
A knowledge-based system (KBS) is a part of artificial
intelligence that emulates problem-solving processes of
human experts in a specific domain. In many cases, a
KBS consists of a user interface, knowledge base and
inference engine. The knowledge base contains the rules
and facts derived from human experts, while the infer-
ence engine performs the logical deduction reasoning and
knowledge synthesis, and generates solutions to a given
problem. The KBSs are especially useful when the solu-
tion of problems mainly relies on the empirical knowl-
edge of human experts, when multiple solutions are in
consideration [1]. These systems are not new in RS im-
age analysis and in many cases which have been used for
automatic image understanding and interpretation [2].
Different types of these systems have been and are being
developed depending upon the solutions of the given pro-
blems and the structures of knowledge representation [3].
In general, the most commonly quoted problems for
development of the KBSs are the unavailability of the
good experts and knowledge engineers as well as the
difficulties with the rule extraction process. In other
words, it is a problem of knowledge acquisition [4]. As
the main task of a KBS is to provide solutions to a prob-
lem in a specific domain, utilizing the knowledge and
expertise embodied in it, the necessary knowledge should
be mainly extracted from human experts throug h a know-
ledge acquisition process. Also, such knowledge may be
taken from other sources such as literature in a given pro-
blem domain or other field and empirical data sets [5].
For the optical RS data sets, a set of knowledge can be
acquired from the reflective and emissive properties of
objects or classes of interest, whereas for the microwave
images such knowledge could be acquired from the back-
scatter properties as well as natural and man-made fea-
At microwave wavelengths, three types of scattering
such as surface scattering, volume scattering, and doub le
bounce scattering occur, and most radar image analyses
are based on them. If the surface is homogeneous, then
surface scattering will occur and it can b e either specular
or diffuse, or intermediate depending on the wavelength
and surface roughness. If the surface is dielectrically in-
homogeneous, then volume scattering where radar pene-
trates the surface and the return is due to scattering from
the underlying materials, will occur. Double bounce
scattering occurs, when the right angles are formed be-
tween natural and artificial objects. In addition, radar
interpretations are environment or site specific and rela-
tive to the frequency as well as polarization, incidence
angle, surface properties and the effects of water and soil
moisture. The tonal variations on the radar images also
depend on the changes of the boundary condition be-
tween spec ular and di ffuse sca t tering [6 , 7].
opyright © 2013 SciRes. ARS
The aim of this study is to analyze the backscattering
characteristics of different urban land cover types used
for a knowledge acquisition process and describe an ap-
propriate technique for the acquired knowledge repre-
sentation. For this purpose, different land cover classes
have been selected from TerraSAR X-band, Envisat
C-band and Palsar L-band radar images of urban area in
Mongolia, and analyzed in relation to the surface and
system parameters. For the final analysis, the grey level
values of a group of contextually dependent pixels se-
lected from different parts of the images have been used
and compared on the basis of the mean values (M) and
standard deviation (SD).
2. Test Site and Data Sources
As a test site, Ulaanbaatar, the capital city of Mongolia
has been selected. The selected part of the city is charac-
terized by such main classes as building area, ger area
(Mongolian traditional dwelling), forest, grass, soil with
high moisture, soil with low moisture, open area and wa-
ter. The building area includes buildings of different
sizes, while ger area includes mainly gers surrounded by
fences. The forest class consists of different types of tall
and short trees located along the Tuul River. The grass
mainly includes grassland area, but there are some bush
and short trees, too. The soil with high moisture is lo-
cated along the area which previously was a river valley.
The soil with low moisture is mainly distributed along
the northern range of the Tuul River. The water class
represents the Tuul River located in the southern part of
Ulaanbaatar. Figure 1 shows an Env isat image of the test
site, and some examples of its land cover.
In the present study, the data consisted of TerraSAR
X-band HH polarization image of March 2008 with a
spatial resolution of 1m, Envisat C-band HH polarization
image of March 2010 with a spatial resolution of 25 m,
Figure 1. 2011 Envisat image of the selected part of Ulaan-
baatar. 1—building area, 2—ger area, 3—forest, 4—grass,
5—soil with high moisture, 6—soil with low moisture,
7—open area and 8—water.
and Palsar L-band HH polarization image of May 2006
with a spatial resolution of 25 m. Also, as additional
ground truth information a topographic map of 2000,
scale 1:5000, multichannel SPOT XS image of 2009 as
well as soil and vegetation maps of scale 1:100.000 were
3. Knowledge Acquisition
In general, knowledge acquisition is used for an initial
intelligent guess of the spectral v alues of selected classes
and it is important for selection of the reliable features as
well as for definition of reliable spatial and spectral
thresholds. In the present study, knowledge acquisition
has been conducted ba sed o n the th eor y of b ackscattering
mechanisms of each class available within the selected
image frame.
Initially, from different parts of the SAR images, poly-
gons representing the selected land cover types have
been selected using local knowledge. Then, the polygons
were transformed into primary signatures (ERDAS 1999)
of the representative classes. As the images have high
and very high spatial resolutions, the final signatures
included different numbers of pixels. The signature se-
lected sites from TerraSAR and Palsar images are shown
in Figures 2 and 3, and the mean valu es and SDs for th e
chosen training samples selected from the multi-fre-
quency SAR images are shown in Table 1.
As seen from Table 1, the building area has the high-
est mean values in X and C bands and the second highest
mean value in L band. It is statistically separable from
most classes, but in X band its backscatter values are
highly scattered. Compared to the building area, a ger
area might easily have some overlaps with other classes
specifically in X band, unless accurate spectral threshold
values are selected. In general, the backscatter from ur-
Figure 2. The signature selected sites in TerraSAR image of
Ulaanbaatar. 1—building area, 2—ger area, 3—forest,
4—grass, 5—soil with high moisture, 6—soil with low mois-
ture, 7—open area and 8-water.
Copyright © 2013 SciRes. ARS
Figure 3. The signature selected sites in Palsar image of
Ulaanbaatar. 1—building area, 2—ger area, 3—forest,
4—grass, 5—soil with high moisture, 6—soil with low mois-
ture, 7—open area and 8-water.
Table 1. The mean backscatter values of the selected land
cover classes and their variations in the TerraSAR and
Quickbird bands.
TerraSAR Envisat Palsar
No Classes M SD M SD MSD
1 Building area 175.2 71.3 231.6 33.2 201.641.2
2 Ger area 132.2 64.4 190.9 30.3 247.314.4
3 Forest 90.9 42.1 122.2 19.9 168.839.9
4 Grass 71.4 28.3 95.3 18.9 151.543.5
5 Soil with high moisture 147.3 50.2 134.3 23.3 115.438.1
6 Soil with low moisture 63.2 21.1 42.0 20.4 23.522.1
7 Open area 57.1 17.5 82.5 19.7 19.816.1
8 Water 89.1 29.1 29.2 13.1 37.823.5
ban areas should contain information about street align-
ment, building size, density, roofing material, its orienta-
tion as well as vegetation and soil, thus resulting in all
kinds of scattering. Roads and buildings in urban areas
can reflect a larger component of radiation, if they are
aligned at right angles to the incident radiation. Here, the
intersection of a road and a building tends to act as a
corner reflector. The amount of backscatter is very sensi-
tive to street alignment. The areas of streets and build-
ings aligned at right angles to the incident radiation will
have a saturated very bright appearance and non-aligned
areas will have a bright/dark appearance in the resulting
image. Volume and surface scattering will also play an
important role in the response from many of the urban
features [3,7]. Using Rayleigh’s criterion of surface rough-
ness, for the Ter raSAR X (3.1 cm) band data, the bound-
ary between the diffuse and specular reflection can be
determined as being between 0.38 cm and 1.45 cm, while
for the Envisat C (5.6 cm) band data it could be in be-
tween 0.75 cm and 0.79 cm, depending on the incident
angles. Many urban surfaces have variations that are just
greater or less than these values.
To form the signatures of ger area and building classes,
the polygons representing these classes were selected
from more homogeneous parts of the images. However,
the investigation of the individual pixels indicated that
those objects aligned at right angles gave extremely high
backscatter values and they reached their maximum in all
X, C and L bands. In addition, as seen from Table 1, at
X-band frequency ger area almost overlaps with soil with
high moisture, but at C and L-band frequencies the two
classes are completely separable. This is most probably
due to the fact that soil moisture is more saturated at the
upper 3 cm (penetrating capability of X-band) of th e soil
causing high backscatter return at X-band frequency,
while at the depth of 6 cm - 23 cm (penetrating capability
of C and L-bands) the soil surface condition is more ho-
mogeneous causing moderate backscatter return.
In case of forest, at X and C band frequencies, canopy
scattering and attenuation will be caused primarily by
leaves and needles, because the wavelength is too short
to penetrate into the forest layer. However, at L-band
frequency the wavelength will penetrate to the forest
canopy and will cause volume scattering to be derived
from multiple-path reflections among twigs, branches,
trunks and ground. As seen from Table 1, the forest has
moderate backscatter return and it has some statistical
overlaps with the grass in L band. The grass will act as
mixtures of small bush, grass and soil and the backscatter
will depend on the volume of either of them [6]. Al-
though plant geometry, density and water content are the
main factors influencing the backscatter coming from the
vegetation cover, ground truth information revealed that
the contribution of vegetation is not very significant dur-
ing this time of the year. Comparing the mean values of
forest and grass, one can observe that they are more
separable at short radar bands due to stronger volume
scattering in the forest area at those frequencies.
There are two soil classes having different backscatter
values. As seen, the soil with low moisture forms more
compact signatures, whereas the soil with high moisture
forms scattered clusters. The backscatter of soil depends
on the surface roughness, texture, existing surface pat-
terns, moisture content, as well as wavelength and inci-
dent angle. The presence of water strongly affects the
microwave emissivity and reflectivity of a soil layer. At
low moisture levels there is a low increase in the dielec-
tric constant. Above a critical amount, the dielectric con-
stant rises rapidly. This increase occurs when moisture
begins to operate in a free space and the capacity of a soil
to hold and retain moisture is directly related to the tex-
Copyright © 2013 SciRes. ARS
ture and structure of the soil [3,6 ]. As it can be seen from
Table 1, soil with low moisture has lower values in
comparison with all other classes. This indicates lower
backscatter intensities caused by specular reflection due
to lack of some surface features, low roughness proper-
ties and low dielectric constant of the dry soil. In contrast
to the soil with low moisture, wet soil gives high back-
scatter return compared to the most of the classes be-
cau se o f the soil moisture content and increased dielectric
In most cases, open area will behave as a specular re-
flector, but in short wavelengths and at some specific
conditions where sufficient surface roughness is ob-
served, it will have some components of diffuse scatter-
ing. As seen from Table 1, at SAR wavelengths the open
area has very low average backscatter return compared to
almost all other classes and it has a high statistical over-
lap with soil with low moisture in X and L bands. Spe-
cifically, in L-band, its backscatter values are lower than
the water, which means that the selected site is totally
dominated by a specular reflection and there is very little
return signal toward the radar antenna.
Generally, in an urban environment, most of the avail-
able water resources will have specular reflection and
should appear very dark on images for all incident angles
except 0. To obtain some backscatter from a water sur-
face, it must by some mechanism, be made rough. The
principal mechanism for the roughening the water sur-
face is the generation of waves and in reality the waves
can be generated by strong wind. However, in the given
case, there is contextual influence of grass, green vegeta-
tion and small bush (from both sides of the river) due to
which there is increased backscatter return (especially in
4. The Proposed Knowledge Representation
In general, urban areas are very diverse and create com-
plex systems that have very explicit characteristics. These
make the urban system possible to distinguish from all
other systems. For example, it is necessary to consider
the physical aspects such as size, structural aspects such
as composition, economic aspects such as cost, and en-
vironmental aspects such as relation with its env ironment
as well as the aspects connected to its operation (i.e.,
utilization by people, interaction with concerned users).
In everyday life, an organization or a municipality man-
ages an urban system and their work consists of arrang-
ing, planning and managing these urban systems [8]. There-
fore, development of the urban knowledge base requires
a complex approach that considers all urban aspects.
For development of a proper KBS used for the auto-
matic interpretation of such multi-frequency SAR images,
the above knowledge about backscattering properties of
different natural and artificial objects of urban environ-
ment can be represented in a most efficient way, for ex-
ample, using a rule-based approach. It is one of the most
commonly used knowledge representation technique, in
which different rules mainly containing the constraints
on expert-defined variables, spatial objects, external pro-
grams and other spatial models are constructed and used
for the hypothesis evaluation [9]. Thus, different para-
meters to be required might be formulated as a set of “IF
THEN” rules and the actual image processing can be done
on the basis of a forward chaining principle [10]. More-
over, the rule-based inference based on the forward chain-
ing principle can be mapped into neural network archi-
tecture. For this purpose, at first, data attributes should be
assigned to input nodes, final hypotheses should be as-
signed to output nodes and the hypotheses must be as-
signed to hidden units. Then, the initial domain rules
determine how the attributes and hypotheses are linked
and how the links are weighted.
5. Conclusion
The aim of this study was to conduct kn owledge acquisi-
tion through the analysis of the backscattering properties
of the urban land cover features in Mongolia using Ter-
raSAR X-band, Envisat C-band and Palsar L-band SAR
images. In case of equal calibration, it was possible to
compare the backscattering properties of the separate
radar bands. However, as the calibration of each SAR
image was different, a comparison was made among the
classes within the bands themselves. Within the frame-
work of the study, an appropriate technique, which is a
rule-based approach for the acquired knowledge repre-
sentation was proposed. Overall, the study demonstrated
that the multi-frequency radar images could be success-
fully used for the knowledge acquisition as well as for
the investigation of different urban land surface features.
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