# Scattering-Based Model of the SAR Signatures of  Complex Targets for Classification Applications

Authors: Gerard Margarit; Jordi J. Mallorqui
Journal: International Journal of Navigation and Observation
 (2008)

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## Abstract

The modeling of complex target response in SAR imagery is the main subject of this paper. The analysis of a large database of SAR images with polarimetric and interferometric capabilities is used to accurately establish how the different structural parts of targets interact with the incident signal. This allows to relate the reflectivity information provided by SAR images with specific geometries and to fix variation reflectivity patterns in terms of different imaging parameters such  as image resolution, incidence angle, or operating frequency. Most of the used images have been obtained from the SAR simulator of complex targets developed at UPC, which is able to generate realistic data for a wide range of observation and environmental conditions. The result is a precise scattering-based SAR model that opens the door, among others, to an alternative way for reliable geometry retrieval. Under this approach, a novel SAR classification method for ships has been proposed. The preliminary evaluation in simulated scenarios shows a notable classification capability even under strong clutter and ship motion conditions. Due to these promising results, the same methodology is intended to be applied to urban areas. Concerns about possible model limitations and required improvements are preliminarily treated.


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## Body

## 1. Introduction

The proliferation of new SAR sensors  with improved
capabilities in  terms of system resolution and information channels (polarimetry and interferometry) has increased
the range of products that can be obtained from SAR imagery [1]. Examples are
characterization and classification of man-made structures like ships or urban
targets [2–5]. In the past, the
limitations of sensors' performance have made it almost impossible to precisely
retrieve those physical and geometrical parameters related with the imaged
targets. This trend is currently changing with the new generation of airborne
and orbital  sensors.Now, real data have higher diversity, as polarimetric channels are available, and increased revisiting times. Their adequate interpretation requires the development of new models,
where
the SAR signatures of complex targets become related with their geometries. The
development of such models with real imagery is difficult and arduous because
the required amount of data with the related ground-truth is not always
available and can be too expensive to generate. A feasible alternative lies in
the usage of realistic simulators, where flexibility and reduced cost can
overcome previous limitations allowing to rapidly increase the knowledge in
target scattering.In this context, the
current paper provides a new methodology for modeling the response of complex
targets in SAR imagery based on the analysis of large stacks of simulated scattering
maps [6]. By using an
accurate and realistic SAR simulator, the response of each structural part of a
target can be described in terms of the three-dimensional position, the
scattering properties (reflectivity intensity and polarimetric behavior), and
the dependence of these properties with respect to key imaging parameters
(incidence angle and target orientation, resolution, operating frequency,
etc.). All this information can help to predict the expected real SAR response
for a given set of observation conditions and, hence, geometrical and physical
information may be retrieved by simply inverting the relations established
within simulation environments. As a result, a new target model emerges, which
can be exploited in a large number of applications.The previous  modeling methodology has been applied to
ships [7] and urban
targets [8]. This has
been done with GRECOSAR [9], a simulator able to realistically simulate SAR images
of complex targets within flexible and controlled scenarios. It works with 3D
high-frequency ElectroMagnetic (EM) calculations using physical optics (PO) and
physical theory of diffraction (PTD) [10, 11]. In a first step, a large number of fully
polarimetric scattering maps (inverse SAR, ISAR) with resolution around
centimeters have been used to study the dispersion behavior of targets in terms
of their geometries and observation conditions. This has allowed to observe a
specific scattering pattern from which the dispersion information can be linked
with target geometry for a large range of views. In a second step, the
signatures of targets have been analyzed in simulated SAR images. For an
adequate image resolution, it follows that the previous scattering patterns can
be related with the observed structures or, in other words, targets' geometry
can be inferred from the scattering patterns.As a result, the proposed scattering-based model of
complex targets appears to be feasible for practical applications. Its
usefulness in real scenarios has been evaluated by testing its potentialities
for basing new classification/identification approaches. Special attention is
placed on ships where the availability of robust algorithms is mandatory for
complementing new ship monitoring techniques devoted to SAR platforms (see
European IMPAST [12],
DECLIMS [13], and
LIMES [14] projects).
In this sense, the vessel classification algorithm (VCA) working with
polarimetric interferometric SAR (PolInSAR)  imagery is presented [6]. Its operating principle is
briefly described and some tests are carried out within simulated scenarios
for the analyzed real-like observation conditions.
The results show notable classification ratios even under adverse observation
conditions, mainly manifested by sea clutter and ship motions [7]. Regarding urban
environments, the conclusions derived from the model are used to improve
real-data interpretation and the performance of interferometric-related
applications such as subsidence.This paper is structured as follows. Section2
describes the modeling methodology stressing its advantages and limitations
with respect to classical approaches. Section 3 presents samples of the
scattering maps used to relate the model scattering behavior with its geometry.
The reliability of the derived scattering patterns in SAR images is assessed in
Section 4. They are exploited in Section 5, where the VCA algorithm and new
guidelines for helping to improve the interpretation of urban data are
introduced.


## 2. Modeling Methodology

Modeling  the dispersion response of complex targets
should take into account what follows: (1) the measuring variables (model
inputs, i.e., polarimetric and interferometric data), (2) the parameters to
be inverted (model outputs), and (3) the
main application for which target modeling is
sought. In our context, complex target modeling is
conceived for characterization and classification and, hence, the retrieval of
key geometrical information is the main goal (model output). To
successfully do this process, accurate geometry-scattering relations are
mandatory so that the reflectivity information of SAR images can be connected
with specific geometrical shapes. For such purpose, large datasets have to be
analyzed for the largest possible scenario diversity. In real scenarios, this
task is extremely complex as, besides the long and costly measurement
campaigns, the coordination between the sensor and testing
scenarios should be almost perfect for reaching the
planned observation conditions and acquiring accurate ground-truth. In most
situations, for instance, with vessels at sea, this is almost impossible.Recent works have suggested the possibility to exploit
numerical tools for carrying out, in a first stage, the previous studies
[7, 9]. Simulation presents two
advantages: (1) the capability to quickly process a wide range of scenarios for
which target models can be developed, and (2) the possibility to plan
measurement campaigns, where observation conditions allow to efficiently test,
improve, and/or validate the proposed model. The combination of both can lead
to better models.In this study, simulated images have been used. They
have been obtained from the GRECOSAR numerical tool [9], which is able to reproduce
in simple PC realistic SAR images of complex targets. It is based on UPC's
GRaphical Electromagnetic COmputing (GRECO) solver [10]
that estimates, for each single frequency, the radar cross-section (RCS) of 3D
targets via high-frequency methods (PO, PTD). Targets are modeled with the CAD
package GiD of the International Center of Numerical Methods for Engineering
(CIMNE) [15]. Computer
efficiency and scenario flexibility are the main advantages of the simulator.
This allows to simulate any sensor for any operating band, mode, and resolution
with polarimetric and/or interferometric capabilities. Target environment is
configurable with dielectric materials, relative sensor-to-target orientation,
incidence angle, and, in the case of vessels, speed, motion dynamics, and
dynamic sea surfaces [16, 17]. Exhaustive tests performed with both canonical and
complex targets have validated the code. Its potentialities for complex target
studies have been shown in previous works devoted to ships [6, 7].


### 2.1. Model Outputs

For classification applications, the parameters to
be inverted have to be related with target's
geometry. It would be desirable to obtain from data the three-dimensional
position of all points defining the structure. However, this is not possible with
radars as only a reduced group of geometrical shapes with high RCS is present
in the image [2, 7, 18].
The remaining scatterers are either cluttered by the stronger ones due to the
limited system resolution or have an RCS below the noise floor.In this framework, the model is focused to find the 3D
position of a set of significant scatterers that summarize the macroscale
geometry of the different types of targets, like those obtained from
polarimetric decompositions. Such scatterers are termed aspermanent
polarimetric scatterers (PePSs) in the sense that they present
similar scattering properties within a wide range of observation conditions.
For each target model, particular combinations of PePSs are selected according
to the analysis of simulated scattering maps derived for different observation
conditions. Mathematically, the feature vector for target j can be denoted
by(1)Θj={Θij}for1<i<NPePS,where NPePS points to the number of PePSs for that target
and(2)Θij={ai,gri,hi,pi}jgathers the azimuth (ai) and ground-range (gri) locations of
scatter i,
jointly with its height (hi) and related scattering mechanism (pi). Scattering mechanisms are defined
according to the basic Pauli mechanisms [19], namely, “trihedral” for those mechanisms with odd
number of reflections (sphere, flat planes, 3D corners, etc.), “dihedral” for
those mechanisms with even number of reflections (2D corners), and
“antisymmetric” for those mechanisms without the symmetry property. All three
follow the orthogonality property and have appeared to be indispensable for
increasing the discrimination capability among different PePS configurations [6].
This explains the importance of the polarimetric term pi in (2). Other polarimetric decompositions
could also be used, but orthogonality helps to better discriminate the
interferometric phase information related to each mechanism.


### 2.2. Model Inputs

The retrieval of the previous
information needs frompolarimetric
interferometric SAR (PolInSAR) imagery. Certainly, SAR
imagery provides information about azimuth (ai) and ground-range (gri) scatter
locations, SAR interferometry (InSAR) [20] about the relative height
among scatterers (hi), and, finally, SAR polarimetry (PolSAR)
[19, 21, 22] about the associated
scattering mechanisms (pi). These relations are valid if each
resolution cell does not contain more than one PePS with the same
scattering mechanism. This implies that (1) it is possible to discriminate a
maximum of three scatterers with different scattering mechanisms within the
same resolution cell, and (2) a minimum of resolution is required for an
adequate PePS discrimination. Different tests have shown that a
resolution around 3 m seems to be enough for ship classification applications
[6, 7].The sensor requirements that imply the usage of
PolInSAR with high resolution are quite demanding [17]. Certainly, the management
of six information channels in the time slot of one synthetic aperture position
increases the hardware complexity and, even worst, imposes important
restrictions on the coverage (swath is severely limited if compared with
single-pol SAR). By now, this can only be assumed by airborne sensors, despite
the fact that the new operating modes of TerraSAR-X and Radarsat-2 are close to
this goal if single-pass interferometry is not considered. In order to make an
efficient usage of the available information, some alternatives are now under
consideration, such as alternate polarimetric (AP) modes or partial
polarimetry (PP) [23–25]. They allow to reduce the number of polarimetric
channels but at the expense of some model approximations that in some cases
cannot be applied, as in the case of ships against strong clutter conditions
[17].


### 2.3. Modeling Scheme

The scheme of the adopted modeling
methodology can be summarized in the following points.(i)
Select a set of targets that can be considered
as representatives of the different types that can be found.(ii)
Generate a large number of scattering maps
with numerical tools that help to fix the scattering behavior of the different
parts of targets' structure. These data have to be derived for a particular set
of observation conditions (Φk) and should evaluate the impact of the
surrounding environment. In a first approach, Φk is defined for the incidence angle (ϕ[°]), relative target-to-sensor orientation (β[°]), and operating frequency (f[Ghz]) taking different values within a fixed range.(iii)
For a given set  of observation conditionsΦk={ϕ,β,f}k,
define a particular feature set Θj for the type of target j. PePSs are selected by those scatterers presenting an RCS 10 dB higher than
that of the surrounding scatterers and a stable polarimetric behavior within a
solid angle of at least π/3 steradian (this is the solid
angle subtended by a cone of 30° of aperture). Three issues are important in this definition: (1) stable polarimetric
behavior for PePS i means  to take the same value of pij within the selected aspect angle (see (4) and
(5)); (2) PePS 3D positions are expressed with respect to a local
coordinate system within the target from which the migration to SAR location
parameters (aij and grij) is performed according to the particular
imaging geometry (see Figure 1); (3) the threshold in the angular aperture is
selected by the response of typical canonical scatterers, like trihedrals and
dihedrals. This implies that the modeling methodology is focused to locate
those structures with the closest scattering behavior to canonical targets.Figure 1
Local
coordinate system used to define PePS within targets. The center is located at
target's center of mass.


### 2.4. Inversion Scheme

Model inversion implies basically to retrieve the
information ofΘij for all the potential candidates of PePS.
It assumes that target signature has been previously isolated from the
environment with any of the currently available detection methods (see
[26, 27] for ships and [5, 28, 29] for urban areas). In a
first iteration, all local maxima present in target's signature for all
polarimetric channels are selected (Nlm) (see Section 5.1). For each one, 〈Θnj〉 is estimated according to (1) with 1<n<Nlm.
The formulae required for this process are listed  as follows. Regarding
height,(3)〈hnj〉=sinϕ[cro4πfB⊥Δψ−Δrtanϕ],where Δψ and Δr fix the interferometric phase and slant-range
difference of scatter n with respect to a fixed reference. ro is the range, and B⊥ is the perpendicular baseline, both known as
radar parameters [20, 30]. Interferometric phase corresponds to the phase of
the pixel of the interferogram related to scatter n.
The interferogram is built by multiplying the master image with the complex
conjugate of the slave for a particular polarimetric channel [20].The polarimetric termpi is estimated according to the significance of
the Pauli scattering mechanism associated with the analyzed scatter n.
This implies the following relations:(4)〈pnj〉=0if|pn,0j|>|pn,1j|,|pn,2j|,〈pnj〉=1if|pn,1j|>|pn,0j|,|pn,2j|,〈pnj〉=2if|pn,2j|>|pn,0j|,|pn,1j|,where |pn,xxj|=pn,xxj/max{pn,0j,pn,1j,pn,2j} defines the
normalized significance of the three Pauli mechanisms:(5)pn,0j=12Shhn+Svvn2,pn,1j=12Shhn−Svvn2,pn,2j=12Shvnfor the elements of the
monostatic master scattering matrix [S]n measured in the pixel of scatter n :(6)[S]n=[ShhShvShvSvv]n.The termxx={0,1,2} is related to the “trihedral,” “dihedral,”
and “antisymmetric” Pauli mechanisms. Finally, azimuth and ground-range
locations are obtained from the azimuth (an,SARj) and slant-range (srn,SARj) positions of
scatter n in the SAR image:(7)〈anj〉=an,SARj,〈grnj〉=srn,SARj+〈hnj〉cosϕsinϕ.The inversion process ends by selecting those PePSs
whose combination better matches the list  defined byΘj.


## 2.1. Model Outputs

For classification applications, the parameters to
be inverted have to be related with target's
geometry. It would be desirable to obtain from data the three-dimensional
position of all points defining the structure. However, this is not possible with
radars as only a reduced group of geometrical shapes with high RCS is present
in the image [2, 7, 18].
The remaining scatterers are either cluttered by the stronger ones due to the
limited system resolution or have an RCS below the noise floor.In this framework, the model is focused to find the 3D
position of a set of significant scatterers that summarize the macroscale
geometry of the different types of targets, like those obtained from
polarimetric decompositions. Such scatterers are termed aspermanent
polarimetric scatterers (PePSs) in the sense that they present
similar scattering properties within a wide range of observation conditions.
For each target model, particular combinations of PePSs are selected according
to the analysis of simulated scattering maps derived for different observation
conditions. Mathematically, the feature vector for target j can be denoted
by(1)Θj={Θij}for1<i<NPePS,where NPePS points to the number of PePSs for that target
and(2)Θij={ai,gri,hi,pi}jgathers the azimuth (ai) and ground-range (gri) locations of
scatter i,
jointly with its height (hi) and related scattering mechanism (pi). Scattering mechanisms are defined
according to the basic Pauli mechanisms [19], namely, “trihedral” for those mechanisms with odd
number of reflections (sphere, flat planes, 3D corners, etc.), “dihedral” for
those mechanisms with even number of reflections (2D corners), and
“antisymmetric” for those mechanisms without the symmetry property. All three
follow the orthogonality property and have appeared to be indispensable for
increasing the discrimination capability among different PePS configurations [6].
This explains the importance of the polarimetric term pi in (2). Other polarimetric decompositions
could also be used, but orthogonality helps to better discriminate the
interferometric phase information related to each mechanism.


## 2.2. Model Inputs

The retrieval of the previous
information needs frompolarimetric
interferometric SAR (PolInSAR) imagery. Certainly, SAR
imagery provides information about azimuth (ai) and ground-range (gri) scatter
locations, SAR interferometry (InSAR) [20] about the relative height
among scatterers (hi), and, finally, SAR polarimetry (PolSAR)
[19, 21, 22] about the associated
scattering mechanisms (pi). These relations are valid if each
resolution cell does not contain more than one PePS with the same
scattering mechanism. This implies that (1) it is possible to discriminate a
maximum of three scatterers with different scattering mechanisms within the
same resolution cell, and (2) a minimum of resolution is required for an
adequate PePS discrimination. Different tests have shown that a
resolution around 3 m seems to be enough for ship classification applications
[6, 7].The sensor requirements that imply the usage of
PolInSAR with high resolution are quite demanding [17]. Certainly, the management
of six information channels in the time slot of one synthetic aperture position
increases the hardware complexity and, even worst, imposes important
restrictions on the coverage (swath is severely limited if compared with
single-pol SAR). By now, this can only be assumed by airborne sensors, despite
the fact that the new operating modes of TerraSAR-X and Radarsat-2 are close to
this goal if single-pass interferometry is not considered. In order to make an
efficient usage of the available information, some alternatives are now under
consideration, such as alternate polarimetric (AP) modes or partial
polarimetry (PP) [23–25]. They allow to reduce the number of polarimetric
channels but at the expense of some model approximations that in some cases
cannot be applied, as in the case of ships against strong clutter conditions
[17].


## 2.3. Modeling Scheme

The scheme of the adopted modeling
methodology can be summarized in the following points.(i)
Select a set of targets that can be considered
as representatives of the different types that can be found.(ii)
Generate a large number of scattering maps
with numerical tools that help to fix the scattering behavior of the different
parts of targets' structure. These data have to be derived for a particular set
of observation conditions (Φk) and should evaluate the impact of the
surrounding environment. In a first approach, Φk is defined for the incidence angle (ϕ[°]), relative target-to-sensor orientation (β[°]), and operating frequency (f[Ghz]) taking different values within a fixed range.(iii)
For a given set  of observation conditionsΦk={ϕ,β,f}k,
define a particular feature set Θj for the type of target j. PePSs are selected by those scatterers presenting an RCS 10 dB higher than
that of the surrounding scatterers and a stable polarimetric behavior within a
solid angle of at least π/3 steradian (this is the solid
angle subtended by a cone of 30° of aperture). Three issues are important in this definition: (1) stable polarimetric
behavior for PePS i means  to take the same value of pij within the selected aspect angle (see (4) and
(5)); (2) PePS 3D positions are expressed with respect to a local
coordinate system within the target from which the migration to SAR location
parameters (aij and grij) is performed according to the particular
imaging geometry (see Figure 1); (3) the threshold in the angular aperture is
selected by the response of typical canonical scatterers, like trihedrals and
dihedrals. This implies that the modeling methodology is focused to locate
those structures with the closest scattering behavior to canonical targets.Figure 1
Local
coordinate system used to define PePS within targets. The center is located at
target's center of mass.


## 2.4. Inversion Scheme

Model inversion implies basically to retrieve the
information ofΘij for all the potential candidates of PePS.
It assumes that target signature has been previously isolated from the
environment with any of the currently available detection methods (see
[26, 27] for ships and [5, 28, 29] for urban areas). In a
first iteration, all local maxima present in target's signature for all
polarimetric channels are selected (Nlm) (see Section 5.1). For each one, 〈Θnj〉 is estimated according to (1) with 1<n<Nlm.
The formulae required for this process are listed  as follows. Regarding
height,(3)〈hnj〉=sinϕ[cro4πfB⊥Δψ−Δrtanϕ],where Δψ and Δr fix the interferometric phase and slant-range
difference of scatter n with respect to a fixed reference. ro is the range, and B⊥ is the perpendicular baseline, both known as
radar parameters [20, 30]. Interferometric phase corresponds to the phase of
the pixel of the interferogram related to scatter n.
The interferogram is built by multiplying the master image with the complex
conjugate of the slave for a particular polarimetric channel [20].The polarimetric termpi is estimated according to the significance of
the Pauli scattering mechanism associated with the analyzed scatter n.
This implies the following relations:(4)〈pnj〉=0if|pn,0j|>|pn,1j|,|pn,2j|,〈pnj〉=1if|pn,1j|>|pn,0j|,|pn,2j|,〈pnj〉=2if|pn,2j|>|pn,0j|,|pn,1j|,where |pn,xxj|=pn,xxj/max{pn,0j,pn,1j,pn,2j} defines the
normalized significance of the three Pauli mechanisms:(5)pn,0j=12Shhn+Svvn2,pn,1j=12Shhn−Svvn2,pn,2j=12Shvnfor the elements of the
monostatic master scattering matrix [S]n measured in the pixel of scatter n :(6)[S]n=[ShhShvShvSvv]n.The termxx={0,1,2} is related to the “trihedral,” “dihedral,”
and “antisymmetric” Pauli mechanisms. Finally, azimuth and ground-range
locations are obtained from the azimuth (an,SARj) and slant-range (srn,SARj) positions of
scatter n in the SAR image:(7)〈anj〉=an,SARj,〈grnj〉=srn,SARj+〈hnj〉cosϕsinϕ.The inversion process ends by selecting those PePSs
whose combination better matches the list  defined byΘj.


## 3. Scattering Maps

The modeling methodology starts with the generation of
stacks of scattering maps (Ωjk) defined for particular sets of observation
conditions (Φk) and targets' types (j). This step has been done with GRECOSAR
following the inverse SAR imaging geometry shown in Figure 2. Some samples are
presented in this section for two different classes of targets: ships and
buildings. The intrinsic geometries of both raise some particular scattering
properties that can help to make the modeling process more efficient. For
instance, the symmetric distribution of scatters in the normal direction to the
line of sight (LOS) [18] induces a higher scattering stability within a wide
range of views, and allows to use a unique feature vector (Θj) for characterizing the target. This normally
happens with symmetric targets like ships, which appear to be specially suited
for the modeling methodology [7, 16].Figure 2
ISAR imaging
geometry of GRECOSAR.All the presented scattering maps are fully
polarimetric and depict the normalized Pauli significance (|pn,xxj|) with an RGB-based color code: red→|pn,0j| (first Pauli mechanism), green→|pn,1j| (second Pauli mechanism), and blue→|pn,2j| (third Pauli mechanism). They show the most
representative cases of all those generated (see [6] for further details).


### 3.1. Modeling Ship Responses

Figure3 provides scattering maps at X, C, and L bands
for an incidence angle of ϕ=20° and three different target orientations, β={295,315,335}°.
Three types of ships have been considered: a Spanish fishing vessel of 30 m
long and 7 m wide (target j=S0), an Icelandic fishing vessel of 70 m long
and 15 m wide (target j=S1), and a passenger ferry of 200 m long and 30
m wide (target j=S2). As observed, each type of ship presents a
particular distribution of strong scatters, PePS candidates, which
appear to be stable within a specific range of views.Scattering
maps obtained for the SPA (a), ICE (b), and FER (c) vessels at L, C, and X
bands withβ∈{295,315,335}° and ϕ=20°.
They have been analyzed with the Pauli CTD theorem for a dynamic range of 25 dB. The lengths of the SPA, ICE, and FER ships are 30, 70, and 200 meters.
Circles isolate the PePSs used in the feature vectors of Table 1.
(a)(b)(c)The selection criteria described in Section2.3 have been applied to these datasets and have confirmed that each
ship presents different PePS combination that leads to the feature
vectors summarized in Table 1. They mainly correspond to dihedral interactions
of cylindric structures (like masts and funnels) and trihedral behaviors due to
corner geometries (like buttresses). Such vectors are valid for the set of
simulated observation conditions ΦSO1={10−30°,280−350°,5.3−9.65GHz}SO1 that cover different range of values for
incidence, target orientation, and operating frequency. The margin of validity
in terms of incidence angle and target orientation is notably large and opens
the door for characterizing a vessel with a reduced set of feature vectors. In
Table 1, PePS 3D positions are expressed in the local coordinate system
shown in Figure 3, from which SAR location parameters are derived according to
the incidence and target orientation angles. Such conversion
needs the knowledge of vessels' bearing
that can be extracted from any of the currently available methods [31, 32]. Note also in Table 1 that
the defined PePSs are valid for the complementary range of orientation
values owned to the symmetry of the vessels'
structure.Table 1
Feature
vectors for targetsj=S0, j=S1, and j=S2 and ΦSO1={10−30°,280−350°,5.3−9.65GHz}SO1.
Heights (z) are normalized with respect to the lowest value in order to match
interferometric height conventions.
ΘS0ΘS1ΘiS0xiyizipiΘiS1xiyihipi13.5−100013−20123.5−5002−6−146.513−0.5−82.513−116.514−0.5−24.51—————ΘS2—ΘiS2xiyizipi—112−2500—210−250.51—3982.51—49−82.51—


### 3.2. Modeling Urban Responses

The same procedure is now being conducted for urban
areas. The presented results should be considered preliminarily as the work is
still in progress and the amount of simulated images is not comparable with
that currently available for ships. In this sense, Figure4 presents some
scattering maps similar to those introduced previously, but related to two
different classes of buildings (j=U0 for the upper one and j=U1 for the lower
one). The structures of both buildings are clearly depicted in Figure 6. They
correspond to two structures within a test site, where ground-based SAR
(GB-SAR) measurements were carried out by UPC [33, 34]. This has provided accurate
ground-truth about building structure as well as external elements that may interfere.
The images have been obtained for ϕ=60° and β={190,200}° at X band in order to reproduce the
observation conditions of the GB-SAR sensor.Scattering
maps obtained for two building models at X band withβ∈{190,200}° and ϕ=20°.
They have been analyzed with the Pauli CTD theorem for a dynamic range of 25 dB. The models cover a maximum area of 50 square meters. Circles isolate the
PePSs used in the feature vectors of Table 2.
(a)(b)(c)(d)In the light of the obtained results, it follows that
the response of these targets can be also described by a set of PePSs.
The related feature vectors are summarized in Table2 for which point
positions are expressed in terms of the coordinate system depicted in Figure 4.
Such scatterers are mainly related to frame points located in doors and windows
(trihedral) and punctual wall-street interactions (dihedral). But now the
scattering maps are not as stable as those obtained from vessels. In general
terms, the whole response is appreciably modified (even by eye inspection) from
one orientation to another, despite the fact that some particular PePSs can still be identified. The causes are the extremely complex
environments that make most of the details to be
able to contribute to the scattering map depending on the incidence conditions.Table 2
Feature
vectors forΦUO1={20°,190°,9.65GHz}SO1 and targets j=U0 and j=U1.
Heights (z) are normalized with respect to the lowest value in order to match
interferometric height conventions.
ΘU0ΘU1ΘiU0xiyizipiΘiU1xiyihipi1−825.501−42002−7−32.2024−89.503−6−80038−112.50Therefore, it appears that building discrimination via
target scattering is more difficult as the number of feature vectors demanded
for covering target dispersion increases notably. However, due to the high
realism achieved by GRECOSAR in reproducing the scattering information of real
scenarios [8], other
areas can take profit of these results. Examples may be the development of
differential interferometry- (DInSAR-) related applications for subsidence
monitoring [5] and the
inclusion of polarimetric data provided by the new sensors, like TerraSAR-X or
Radarsat-2.


## 3.1. Modeling Ship Responses

Figure3 provides scattering maps at X, C, and L bands
for an incidence angle of ϕ=20° and three different target orientations, β={295,315,335}°.
Three types of ships have been considered: a Spanish fishing vessel of 30 m
long and 7 m wide (target j=S0), an Icelandic fishing vessel of 70 m long
and 15 m wide (target j=S1), and a passenger ferry of 200 m long and 30
m wide (target j=S2). As observed, each type of ship presents a
particular distribution of strong scatters, PePS candidates, which
appear to be stable within a specific range of views.Scattering
maps obtained for the SPA (a), ICE (b), and FER (c) vessels at L, C, and X
bands withβ∈{295,315,335}° and ϕ=20°.
They have been analyzed with the Pauli CTD theorem for a dynamic range of 25 dB. The lengths of the SPA, ICE, and FER ships are 30, 70, and 200 meters.
Circles isolate the PePSs used in the feature vectors of Table 1.
(a)(b)(c)The selection criteria described in Section2.3 have been applied to these datasets and have confirmed that each
ship presents different PePS combination that leads to the feature
vectors summarized in Table 1. They mainly correspond to dihedral interactions
of cylindric structures (like masts and funnels) and trihedral behaviors due to
corner geometries (like buttresses). Such vectors are valid for the set of
simulated observation conditions ΦSO1={10−30°,280−350°,5.3−9.65GHz}SO1 that cover different range of values for
incidence, target orientation, and operating frequency. The margin of validity
in terms of incidence angle and target orientation is notably large and opens
the door for characterizing a vessel with a reduced set of feature vectors. In
Table 1, PePS 3D positions are expressed in the local coordinate system
shown in Figure 3, from which SAR location parameters are derived according to
the incidence and target orientation angles. Such conversion
needs the knowledge of vessels' bearing
that can be extracted from any of the currently available methods [31, 32]. Note also in Table 1 that
the defined PePSs are valid for the complementary range of orientation
values owned to the symmetry of the vessels'
structure.Table 1
Feature
vectors for targetsj=S0, j=S1, and j=S2 and ΦSO1={10−30°,280−350°,5.3−9.65GHz}SO1.
Heights (z) are normalized with respect to the lowest value in order to match
interferometric height conventions.
ΘS0ΘS1ΘiS0xiyizipiΘiS1xiyihipi13.5−100013−20123.5−5002−6−146.513−0.5−82.513−116.514−0.5−24.51—————ΘS2—ΘiS2xiyizipi—112−2500—210−250.51—3982.51—49−82.51—


## 3.2. Modeling Urban Responses

The same procedure is now being conducted for urban
areas. The presented results should be considered preliminarily as the work is
still in progress and the amount of simulated images is not comparable with
that currently available for ships. In this sense, Figure4 presents some
scattering maps similar to those introduced previously, but related to two
different classes of buildings (j=U0 for the upper one and j=U1 for the lower
one). The structures of both buildings are clearly depicted in Figure 6. They
correspond to two structures within a test site, where ground-based SAR
(GB-SAR) measurements were carried out by UPC [33, 34]. This has provided accurate
ground-truth about building structure as well as external elements that may interfere.
The images have been obtained for ϕ=60° and β={190,200}° at X band in order to reproduce the
observation conditions of the GB-SAR sensor.Scattering
maps obtained for two building models at X band withβ∈{190,200}° and ϕ=20°.
They have been analyzed with the Pauli CTD theorem for a dynamic range of 25 dB. The models cover a maximum area of 50 square meters. Circles isolate the
PePSs used in the feature vectors of Table 2.
(a)(b)(c)(d)In the light of the obtained results, it follows that
the response of these targets can be also described by a set of PePSs.
The related feature vectors are summarized in Table2 for which point
positions are expressed in terms of the coordinate system depicted in Figure 4.
Such scatterers are mainly related to frame points located in doors and windows
(trihedral) and punctual wall-street interactions (dihedral). But now the
scattering maps are not as stable as those obtained from vessels. In general
terms, the whole response is appreciably modified (even by eye inspection) from
one orientation to another, despite the fact that some particular PePSs can still be identified. The causes are the extremely complex
environments that make most of the details to be
able to contribute to the scattering map depending on the incidence conditions.Table 2
Feature
vectors forΦUO1={20°,190°,9.65GHz}SO1 and targets j=U0 and j=U1.
Heights (z) are normalized with respect to the lowest value in order to match
interferometric height conventions.
ΘU0ΘU1ΘiU0xiyizipiΘiU1xiyihipi1−825.501−42002−7−32.2024−89.503−6−80038−112.50Therefore, it appears that building discrimination via
target scattering is more difficult as the number of feature vectors demanded
for covering target dispersion increases notably. However, due to the high
realism achieved by GRECOSAR in reproducing the scattering information of real
scenarios [8], other
areas can take profit of these results. Examples may be the development of
differential interferometry- (DInSAR-) related applications for subsidence
monitoring [5] and the
inclusion of polarimetric data provided by the new sensors, like TerraSAR-X or
Radarsat-2.


## 4. Model Analysis in SAR Images

This section tests the performance of the first model
version generated in the previous section for SAR images. For such purpose,
some of the previous simulations have been rerun for the SAR imaging geometry
of Figure1. A PolInSAR X band sensor covering the previously indicated
incidences and providing a resolution of 2.3 m in azimuth and 1.3 m in range
has been used.


### 4.1. Ship Targets

Some ship samples are presented first in Figure5 by
expressing the significance of each Pauli mechanism in gray scale. The scenario
does not take target environment into account so that qualitative data
interpretation can be done. From all the contemplated bearings (β=[295:10:355]), Figure 5 only presents two of them, β=295,315° (ϕ=20°), which can be considered as representatives
of the remaining ones.Pauli gray
images showing the weight of each Pauli channel for the X band PolInSAR
simulations of ships. The targets arej=S0 (a), j=S1 (b), and j=S2 (c) taking the bearing values of β=295,325°. The arrows locate the reference PePSs gathered in the feature vectors
of Table 1, where positions are transformed into the SAR azimuth slant-range
grid according to the user incidence and bearing angles.
(a)(b)(c)3D scattering maps retrieved with PolInSAR for urban
simulations (ϕ=60°, β=190°). Bold and normal numbers indicate measured
and expected heights for the highlighted scatterers. Model and data
coregistration is made with a set of reference model points and imaging
geometry.
(a)(b)The inspection of these images shows that the
isolation of those PePSs conforming the feature vectors is possible.
They are imaged with the same scattering properties found in the scattering
maps relative to polarimetric behavior, RCS, and spatial distribution. This
implies that the proposed model can be inverted and, thus, valuable geometrical
information is retrieved. In fact, this process has been done following the
explanation of Section2.4 and a confidence estimation of around 80% has been found.However, the previous case is ideal and, normally, real
scenes do not present such level of isolation between sea clutter and target
response. Their interaction is higher and this leads to image corruption that
can put PePS isolation at risk [7, 17]. Next section will further treat this point showing
that a proper image post-processing can assure reliable model inversion even
under adverse observation conditions.


### 4.2. Urban Targets

Something similar has been done for the two building
targets. In this case, PePS analysis is not performed while thinking in
classification (according to previous scattering analysis), but in other
applications, such as subsidence, monitoring via PolInSAR is
performed. There, the isolation of a set of guide scatterers with
stable scattering properties is essential to retrieve accurate estimations of
terrain deformation velocities along slant-range. So, the higher the scattering
characterization is, the better the confidence is achieved. In single
polarimetric SAR, this technique is mature, and now it is being successfully
exploited in real scenarios [35, 36]. However, it has not migrated yet to PolInSAR, and so
the current work would be helpful.In contrast to the case with ships, SAR simulations
are presented in terms of a 3D scattering map, where the relative scatter
height is displayed jointly with the value of|pn,xxj| (the previous RGB color code is also adopted
here). These maps are shown in Figure 6 (ϕ=60°, β=190°) and allow the isolation of almost all the PePSs identified in Section 3.2 with a confidence of ~75%.
With these maps, a type of product similar to the ISAR one, but with less
sensor requirements, can be obtained for scattering analysis [7]. One main advantage is the
possibility to isolate different scatterers within the same resolution cell.
This is advanced in Section 2.2 and related to the increased
discrimination capability supplied by polarimetric channels. This permits to
increase the accuracy in subsidence measurements due to the subpixel resolution
that may be reached in some cells (in fact, those having PePSs, which
can be considered as good candidates for use as subsidence guide scatterers).


## 4.1. Ship Targets

Some ship samples are presented first in Figure5 by
expressing the significance of each Pauli mechanism in gray scale. The scenario
does not take target environment into account so that qualitative data
interpretation can be done. From all the contemplated bearings (β=[295:10:355]), Figure 5 only presents two of them, β=295,315° (ϕ=20°), which can be considered as representatives
of the remaining ones.Pauli gray
images showing the weight of each Pauli channel for the X band PolInSAR
simulations of ships. The targets arej=S0 (a), j=S1 (b), and j=S2 (c) taking the bearing values of β=295,325°. The arrows locate the reference PePSs gathered in the feature vectors
of Table 1, where positions are transformed into the SAR azimuth slant-range
grid according to the user incidence and bearing angles.
(a)(b)(c)3D scattering maps retrieved with PolInSAR for urban
simulations (ϕ=60°, β=190°). Bold and normal numbers indicate measured
and expected heights for the highlighted scatterers. Model and data
coregistration is made with a set of reference model points and imaging
geometry.
(a)(b)The inspection of these images shows that the
isolation of those PePSs conforming the feature vectors is possible.
They are imaged with the same scattering properties found in the scattering
maps relative to polarimetric behavior, RCS, and spatial distribution. This
implies that the proposed model can be inverted and, thus, valuable geometrical
information is retrieved. In fact, this process has been done following the
explanation of Section2.4 and a confidence estimation of around 80% has been found.However, the previous case is ideal and, normally, real
scenes do not present such level of isolation between sea clutter and target
response. Their interaction is higher and this leads to image corruption that
can put PePS isolation at risk [7, 17]. Next section will further treat this point showing
that a proper image post-processing can assure reliable model inversion even
under adverse observation conditions.


## 4.2. Urban Targets

Something similar has been done for the two building
targets. In this case, PePS analysis is not performed while thinking in
classification (according to previous scattering analysis), but in other
applications, such as subsidence, monitoring via PolInSAR is
performed. There, the isolation of a set of guide scatterers with
stable scattering properties is essential to retrieve accurate estimations of
terrain deformation velocities along slant-range. So, the higher the scattering
characterization is, the better the confidence is achieved. In single
polarimetric SAR, this technique is mature, and now it is being successfully
exploited in real scenarios [35, 36]. However, it has not migrated yet to PolInSAR, and so
the current work would be helpful.In contrast to the case with ships, SAR simulations
are presented in terms of a 3D scattering map, where the relative scatter
height is displayed jointly with the value of|pn,xxj| (the previous RGB color code is also adopted
here). These maps are shown in Figure 6 (ϕ=60°, β=190°) and allow the isolation of almost all the PePSs identified in Section 3.2 with a confidence of ~75%.
With these maps, a type of product similar to the ISAR one, but with less
sensor requirements, can be obtained for scattering analysis [7]. One main advantage is the
possibility to isolate different scatterers within the same resolution cell.
This is advanced in Section 2.2 and related to the increased
discrimination capability supplied by polarimetric channels. This permits to
increase the accuracy in subsidence measurements due to the subpixel resolution
that may be reached in some cells (in fact, those having PePSs, which
can be considered as good candidates for use as subsidence guide scatterers).


## 5. Model Exploitation

Once it has been shown that the proposed model of
complex targets can be feasibly inverted in SAR images, the exploration of what
applications can benefit is useful. In the case of ships, the application of
this model can be considered mature, from which the novel VCA method resulted
[7]. For urban
targets, the study is in a preliminary stage and some guidelines for specific
applications can be only provided.


### 5.1. Ship Targets: VCA Algorithm

The operating principle of VCA lies in analyzing the
input PolInSAR dataset with the quad-pol Pauli vector (due to the fact  that
Pauli decomposition is a complete representation, the same simple mechanisms
are isolated in any basis, e.g., linear or circular). This leads to three
different interferograms (one per each Pauli mechanism), from which local
maxima are isolated. All of them are combined in all possible permutations
building estimated feature vectors with different lengths. These vectors are
then correlated with the reference ones associated with specific ships, whose
relative distance is quantified by a similarity parameter,S,
based on an Euclidean norm [7]. The result is a quantitative manner for identifying
the imaged ship with one of those within the available pattern base.VCA performance is evaluated with a set of simulations
similar to those of Figure5. The same X band sensor has been used for a
perpendicular baseline of B⊥=30m and ϕ=20°,
and a resolution of 2.3 m in azimuth and 1.3 m in range [7]. The scenario considers the
three types of ships for bearings ranging from 295 to 355 in steps of 10
degrees, surrounded by sea surface and experimenting ship motions. On the one
hand, sea clutter  is modeled with a two-scale approach for which a specific
height profile is applied to the points of a dielectric discrete surface
[17, 37–39]. The adopted complex
dielectric permittivity is ϵ=75−j⋅27,
which corresponds to sea water with salinity of 35psu and temperature of 25C [40, 41]. On the other hand, rotational and translational
motions during image acquisition are considered by rotating the CAD model at
each azimuth position. In real scenarios, ship motions can lead to important
image distortions that are characterized by azimuth shifts. They normally cause
notable geolocation errors and signature shape distortions [9, 42]. All environmental conditions are summarized in
Tables 3 and 4. Sea main parameters are described in Figure 7, and pitching
and rolling angular velocities are defined counterclockwise with respect to the
rotation axis [9]. In
all cases, ocean waves travel parallel to the ship from stern to bow.Table 3
Scenario
configurations for simulations in Section4. Sea parameters are hl = 1.5 m, hs = 0.1 m, λl = 100 m, λs=λ/(2sinϕ).
ScenarioBearingsMotionsSea surface0[295:10:355]°NoNo1[295:10:355]°Table4No2[295:10:355]°NoTwo-scale3[295:10:355]°Table4Two-scaleTable 4
Rotational motions in the scenarios defined
in Table3. First-order angular velocities are expressed in rad/s.
βδ˙rollδ˙pitchβδ˙rollδ˙pitch295−1.56−0.26335−0.98−1.16305−1.43−0.52345−0.76−1.32315−1.32−0.76355−0.52−1.43325−1.16−0.98———Figure 7
Scheme of the
two-scale sea surface model.Table5 shows the similarity table for the specified
bearings (the remaining ones provide similar results). They gather the
similarity values related to each feature vector when each ship is processed in
the given observation conditions. As observed, all ships are well identified in
almost all situations preserving a reasonable identification confidence against
strong clutter. Two items are important: (1) the azimuth shifts help in some
cases to improve the identification capability; (2) sea clutter appears to be
the most adverse factor dropping the identification confidence. Specially adverse is for target j=S1 whose lack of PePSs in
the first Pauli channel makes the presence of the sea, with dominating
sphere-like behaviors, increase the confusion with respect to the remaining
ships. In general terms, VCA appears to be reliable enough as to
consider its application in real scenarios. Further
simulation studies give confidence to these asseveration and future measurement
campaigns, which should try to provide the required framework for making the
proper tests.Table 5
VCA similarity values,0<S<1, retrieved for the X band PolInSAR
simulations with sea and ship motions.
β=295°|β=315°SPApatICEpatFERpatProcessing SPA0.57|0.440.15|00|0.25Processing ICE0.1|0.70.8|0.440.1|0.25Processing FER0.21|00.3|00.69|0.56


### 5.2. Urban Targets: Data Interpretation

Section4.2 has shown that the proposed model of
complex targets may be useful for complementing single-pol subsidence studies,
and may help in their possible migration to PolSAR systems (rather than
developing classification). In this sense, the main advantage is the
possibility to predict for which situations and geometries PePSs can be
observed and considered as reliable guide scatterers for deformation studies.Historically, urban applications have been related to
orbital monitoring, because the velocity of changes can be assumed by the revisiting
time of satellites, and historical databases for almost the same imaging
geometry can be built. As a result, most geometries are expected to be imaged
for a specific set of well-defined observation parameters and, hence, target
model can be defined in urban environments as in ships. The only consideration
is that feature vectors should meet the imaging geometries of current sensors
rather than being expressed in terms of solid or radar aspect angles. In this
context, feature vectors take sense, which may help, for example, to predict
where the guide scatterers may be located. Such result would give a
complementary reliability estimation of subsidence measurements, as well as
refining and making the searching criteria more efficient.


## 5.1. Ship Targets: VCA Algorithm

The operating principle of VCA lies in analyzing the
input PolInSAR dataset with the quad-pol Pauli vector (due to the fact  that
Pauli decomposition is a complete representation, the same simple mechanisms
are isolated in any basis, e.g., linear or circular). This leads to three
different interferograms (one per each Pauli mechanism), from which local
maxima are isolated. All of them are combined in all possible permutations
building estimated feature vectors with different lengths. These vectors are
then correlated with the reference ones associated with specific ships, whose
relative distance is quantified by a similarity parameter,S,
based on an Euclidean norm [7]. The result is a quantitative manner for identifying
the imaged ship with one of those within the available pattern base.VCA performance is evaluated with a set of simulations
similar to those of Figure5. The same X band sensor has been used for a
perpendicular baseline of B⊥=30m and ϕ=20°,
and a resolution of 2.3 m in azimuth and 1.3 m in range [7]. The scenario considers the
three types of ships for bearings ranging from 295 to 355 in steps of 10
degrees, surrounded by sea surface and experimenting ship motions. On the one
hand, sea clutter  is modeled with a two-scale approach for which a specific
height profile is applied to the points of a dielectric discrete surface
[17, 37–39]. The adopted complex
dielectric permittivity is ϵ=75−j⋅27,
which corresponds to sea water with salinity of 35psu and temperature of 25C [40, 41]. On the other hand, rotational and translational
motions during image acquisition are considered by rotating the CAD model at
each azimuth position. In real scenarios, ship motions can lead to important
image distortions that are characterized by azimuth shifts. They normally cause
notable geolocation errors and signature shape distortions [9, 42]. All environmental conditions are summarized in
Tables 3 and 4. Sea main parameters are described in Figure 7, and pitching
and rolling angular velocities are defined counterclockwise with respect to the
rotation axis [9]. In
all cases, ocean waves travel parallel to the ship from stern to bow.Table 3
Scenario
configurations for simulations in Section4. Sea parameters are hl = 1.5 m, hs = 0.1 m, λl = 100 m, λs=λ/(2sinϕ).
ScenarioBearingsMotionsSea surface0[295:10:355]°NoNo1[295:10:355]°Table4No2[295:10:355]°NoTwo-scale3[295:10:355]°Table4Two-scaleTable 4
Rotational motions in the scenarios defined
in Table3. First-order angular velocities are expressed in rad/s.
βδ˙rollδ˙pitchβδ˙rollδ˙pitch295−1.56−0.26335−0.98−1.16305−1.43−0.52345−0.76−1.32315−1.32−0.76355−0.52−1.43325−1.16−0.98———Figure 7
Scheme of the
two-scale sea surface model.Table5 shows the similarity table for the specified
bearings (the remaining ones provide similar results). They gather the
similarity values related to each feature vector when each ship is processed in
the given observation conditions. As observed, all ships are well identified in
almost all situations preserving a reasonable identification confidence against
strong clutter. Two items are important: (1) the azimuth shifts help in some
cases to improve the identification capability; (2) sea clutter appears to be
the most adverse factor dropping the identification confidence. Specially adverse is for target j=S1 whose lack of PePSs in
the first Pauli channel makes the presence of the sea, with dominating
sphere-like behaviors, increase the confusion with respect to the remaining
ships. In general terms, VCA appears to be reliable enough as to
consider its application in real scenarios. Further
simulation studies give confidence to these asseveration and future measurement
campaigns, which should try to provide the required framework for making the
proper tests.Table 5
VCA similarity values,0<S<1, retrieved for the X band PolInSAR
simulations with sea and ship motions.
β=295°|β=315°SPApatICEpatFERpatProcessing SPA0.57|0.440.15|00|0.25Processing ICE0.1|0.70.8|0.440.1|0.25Processing FER0.21|00.3|00.69|0.56


## 5.2. Urban Targets: Data Interpretation

Section4.2 has shown that the proposed model of
complex targets may be useful for complementing single-pol subsidence studies,
and may help in their possible migration to PolSAR systems (rather than
developing classification). In this sense, the main advantage is the
possibility to predict for which situations and geometries PePSs can be
observed and considered as reliable guide scatterers for deformation studies.Historically, urban applications have been related to
orbital monitoring, because the velocity of changes can be assumed by the revisiting
time of satellites, and historical databases for almost the same imaging
geometry can be built. As a result, most geometries are expected to be imaged
for a specific set of well-defined observation parameters and, hence, target
model can be defined in urban environments as in ships. The only consideration
is that feature vectors should meet the imaging geometries of current sensors
rather than being expressed in terms of solid or radar aspect angles. In this
context, feature vectors take sense, which may help, for example, to predict
where the guide scatterers may be located. Such result would give a
complementary reliability estimation of subsidence measurements, as well as
refining and making the searching criteria more efficient.


## 6. Conclusion

This paper has presented a new modeling methodology
for the SAR signatures of complex targets. The analysis of stacks of simulated
images related to diversified scenarios, sensors, and types of targets is used
to look for certain reference scatterers (PePSs). Such scatterers
accomplish a set of conditions focused to assure the discrimination among
different types of geometries. Their 3D position and polarimetric scattering
mechanism conform the bases of the model feature
vectors, which allows to link the reflectivity information of SAR images with
the macroscale structure of targets.One of the main advantages of the proposed modeling
approach is the minimization of the amount of real images required for testing
the model. Certainly, simulated images can allow, in a first stage, the
generation of preliminary version of models with reduced costs. Following this,
these results would be used to define adequate measurement campaigns and model
tests, which can lead to more refined solutions. In the current work, the first
step has been tackled with the SAR simulator GRECOSAR, which has
appeared to be an efficient tool for such scattering analysis.GRECOSAR has
been used to generate scattering maps for three types of ships and two types of
buildings. The images have shown that PePS isolation is possible, but
with different scattering sensitivity in both types of targets. While the same
feature vector is valid for ships along a large set of observation conditions,
buildings have to be identified with different vectors. The reason is their
higher structure complexity and lack of symmetry.The analysis of these results in SAR images has
confirmed that the current model can be useful for different applications. In
the case of ships, classification techniques may benefit as target
discrimination can be reached via a quantitative and robust manner. This has
been shown by presenting the novel VCA method, which allows, according to tests
performed in simulated scenarios, high identification ratios even against sea
clutter and ship motions. In the past, these items have become important
limitations for classical methods. For urban scenarios, the current model is
useful for fixing structures which are potential candidates for use in
subsidence applications. Improved interpretation of interferometric data is
also an important item, which may benefit the development of height profiles or
better real-data analysis.Therefore, the scattering-based model seems viable for
ships and urban-like targets. Its exploitation in SAR
imagery needs, however, from PolInSAR
sensors with recommended resolutions lower than3m so that PePSs can be properly isolated.
In the case of ships, single-pass modes are very demanding as it is not clear
whether satellite-based solutions could be someday available. On the contrary,
just launched designs as TerraSAR-X and Radarsat-2 can provide repeat-pass
capabilities useful for urban applications. Future works should devote efforts
for evaluating the model in real images so that definitive confidence to the
model and related applications can be reached.



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*Source: 426267-2008-08-04.xml*