SINGLE-LOOK SAR IMAGES AND DETECTION OF SEA DARK AREAS
Attilio Gambardella(1), Ferdinando Nunziata(2), Antonio Sorrentino(2),
Giuseppe Ferrara(2) and Maurizio Migliaccio(2)
(1)
Università degli Studi di Cagliari, Dipartimento di Ingegneria Elettrica ed Elettronica,
Piazza d’Armi 19, 09123 Cagliari, Italy. e-mail: attgamba”at”diee.unica.it.
(2)
Università degli Studi di Napoli “Parthenope”, Dipartimento per le Tecnologie,
Via Medina 40, 80133 Napoli, Italy. e-mail: nunziata”at”uniparthenope.it.
ABSTRACT
In this paper we present a novel approach for the
detection of low backscatter areas and areas with strong
scatterers in marine SLC SAR images. The approach is
based on the use of the three parameters of the
generalized K probability density function. This
speckle model embodies the Rayleigh scattering scene,
which is descriptor of scenes dominated by Bragg
scattering, the Rice scattering scene, which is
descriptor of areas in which a dominant scatter is
present, and the K-distribution, which is descriptor of
non-Gaussian signal statistic which normally
characterize the sea echo from free-area illuminated by
an high-resolution radar. A large data-set of ERS 1/2
SLC SAR images, provided by the ESA under the
CAT-1 Project C1P-2769, is employed. Results show
the effectiveness of the approach.
1. INTRODUCTION
Synthetic Aperture Radar (SAR) is the key sensor for
several marine remote sensing applications. In this
study the stochastic nature of a SAR image is
investigated as an additional source of information to
be exploited to learn more about the underlying
physics. Classical Rayleigh distribution model for the
amplitude of backscattered signal well fits
homogeneous land scenes and the backscattered signal
from sea surface when a large area is illuminated by the
radar. It represents a reference since it is associated to
the so-called fully-developed speckle [1]. Moreover,
when an high-resolution radar illuminates sea surface
large deviations from Rayleigh statistic are often
found. The K probability density function (pdf) has
been found to be a particularly useful model for sea
amplitude statistics [2]. It reduces to the Rayleigh pdf
under appropriate circumstances [2]. Moreover, the K
pdf model has been generalized to model the amplitude
of backscattered signals with a non-uniform phase
distribution [3]-[4].
The proposed model is based on the GK pdf, which
ensures a continuous and physically consistent
transition among different scattering scenarios [3]. This
_____________________________________________________
Proc. ‘Envisat Symposium 2007’, Montreux, Switzerland
23–27 April 2007 (ESA SP-636, July 2007)
speckle model embodies the Rayleigh scattering case,
which is descriptor of scenes dominated by Bragg
scattering, the Rice scattering case, which is descriptor
of areas in which a dominant scatter is present, and the
K-distribution, which is descriptor of non-Gaussian
signal statistic which normally characterize the sea
echo from free-area illuminated by an high-resolution
radar [4].
The rationale of this study is to characterize the
electromagnetic return of selected regions of interest
(ROIs) over Single-Look Complex (SLC) SAR images
by means of the three characteristic parameters of the
GK distribution α, η and ν. Instead of suppressing the
speckle by means of appropriate averaging, which
makes the SAR image spatial resolution coarser, it is
possible to extract useful information from SAR fullresolution images once that the speckle is properly
modeled. As a matter of fact, GK pdf is able to
describe the high-resolution SAR statistical behavior
and is advisable when small low backscatter area (i.e.
dark areas) and strong scatterers need to be detected.
On the applicative view point, this study can assist
classical oil-spill detection techniques. Results show
the effectiveness of the approach once that the
differential GK parameters are considered.
2. THEORY BACKGROUND
According to the two-scale model the surface height is
partitioned into a large-scale and a small-scale
displacement [5]-[7]. Thus, over any patch of the
surface, large compared with small-scale length but
small respect to the large-scale length, the scattering
can be generally modeled as first-order Bragg
scattering from the small inhomogeneities [5]-[7]. The
effect of the large-scale structure is to modulate, both
linearly and non-linearly, the small-scale scattering [2],
[8]. When the dominant mechanism is Bragg scattering
it is possible to assume a fully developed speckle
model for the statistical fluctuations of the
backscattered signal. This is the case of sea surface
when a weak large-scale modulation occurs or the area
illuminated by the radar is too large with respect to the
large-scale structure [2]-[8]. In this case the scattered
field can be written as the sum of N elementary fields;
mathematically it can be seen as a N-step twodimensional random walk in a complex plane [3].
When N is a given number, assuming statistical
independence between the n-th step length {an} and the
associated phase shift {φn}, if {φn} are uniformly
distributed,
the
central-limit
theorem,
for
asymptotically N large, can be exploited and fully
developed speckle regime is achieved [4]. In this case
the scattered field can be described by a zero-mean
circular complex Gaussian process. Hence, the field
amplitude and intensity are Rayleigh and exponentially
distributed, respectively [3]-[4]. However, the above
mentioned assumptions of randomness and statistical
independence, although likely in the case of a confused
short-crested sea, may fail in presence of a large-scale
modulation due to a long-wave field [2]. Thus, under
these conditions, large deviation from Gaussian
statistic is often found, particularly when an highresolution radar, such as SAR, is considered [2]. In this
case the, modulation of small-scale inhomogeneities by
the larger ones leads to the well-known bunching
phenomena, which characterizes sea surface [4], [9][10]. This phenomena can be included in the random
walk taking N as a random number fluctuating
according to a negative binomial (NB) distribution,
ruled by a non-negative bunching parameter α. For
asymptotically large mean step number and under the
hypothesis of strong scattering a non-fully developed
model based on the K-distribution is achieved [3]-[4].
The K pdf, relevant to the intensity of the scattered
field (i ≡ |E|2), is given by [2]:
f (i ) =
2
α
Γ(α )η α +1
α +1
2
⎞
⎛2
Kα −1 ⎜⎜
α i ⎟⎟
⎠
⎝η
,
(1)
where Γ(·) is the Eulerian gamma function, Kα-1(·) is
the modified Bessel function of the second kind of
order α-1 and η is the slope parameter. This is a twoparameters function which has shown a good
agreement in fitting experimental data in a wide range
of non-Gaussian scattering configurations, including
microwave sea echo [2], [4], [8], [11]-[12]. Eq. (1) is a
function of the modified Bessel function of the second
kind and as the order of Kα-1(·) gets larger the NB
distribution, associated to N, tends to a Poisson
distribution and the shape of (1) finally becomes an
exponential pdf, i.e. a Rayleigh pdf for the field
amplitude [3]-[4]. α is unambiguously referred as shape
parameter and can be used as indicator of the degree of
non-Gaussian signal statistics [12] while the η
parameter, under uniform large-scale conditions, is
mainly related to the intensity of the local
backscattering, due to the small-scale inhomogeneities
[8], [11].
According to [3]-[4] it is possible to generalize this
model considering a biased two-dimensional random
walk (i.e. a non-uniform phase distribution). In this
case, for large mean step number, a GK pdf is obtained.
This is a three parameters pdf whose expression, for
the field intensity, is given by [3] :
⎞ 2
⎛
⎟
⎜
α −1
⎟
⎜
α
2α
2
f (i ) =
i
Io ⋅
⎟
⎜
Γ(α )η α +1 ⎜ ⎛ ν 2 ⎞ ⎟
⎜
⎟
1+
⎜ ⎜ 4α ⎟ ⎟
.
⎠⎠
⎝ ⎝
1
⎫
⎧
⎞
⎛ν
⎪ 2 ⎡⎛ ν 2 ⎞ ⎤ 2 ⎪
⎟α i ⎥ ⎬
i ⎟⎟ Kα −1 ⎨ ⎢⎜⎜1 +
⋅ ⎜⎜
4α ⎟⎠ ⎥⎦ ⎪
⎝η ⎠
⎪η ⎢⎣⎝
⎭
⎩
α −1
(2)
α and η are the same parameters shown in (1) and ν
deals with the presence of a dominant scatterer. The ν
parameter is expected to reveal the presence of a nonnegligible coherent component in the backscattered sea
surface signal, due, for example, to a ship.
Since here main concern is on the analysis of the
consistency of the physical approach, only a suboptimal and computer time effective parameter
estimation procedure is considered [13]. According to
such approach ν and η parameters are estimated by
means of the Rice factor, i.e. the coherent to incoherent
received power ratio. The α parameter is estimated by
means of a numerical procedure, based on the χ2 test,
which minimize the L2 error norm between the
measured pdf and the theoretical GK pdf, where the α
parameter is left free [5].
3. EXPERIMENTAL RESULTS
In this section the behavior of the three GK distribution
parameters, as descriptors of marine dark areas and
strong (although small) scatterers over SLC SAR VV
polarized C-band images, is presented and discussed.
The reference data set, i.e. the ERS 1/2 SLC SAR VV
polarized C-band images, has been provided by the
European Space Agency (ESA) under the CAT-1
Project C1P-2769. The nominal resolution is 10 meters
in range and 5 meters in azimuth.
The study has been conducted evaluating the GK
parameters relevant to the different ROIs. In order to
better appreciate the parameters sensitivity, each ROI is
coupled with a reference area. Such area is identical in
size, is a non-dark area and without dominant
scatterers. To further assist the interpretation of the
results, the wind speed is evaluated by the SAR images
[14]-[16]. The evaluation of the wind speed by SAR
data, although sub-optimal [14], allows overcoming the
incorrect time/spatial co-location between SAR data
and operational wind data and retrieves the wind speed
at a finer spatial scale (1-2 Km) [15]-[16].
The first data set is relevant to the acquisition of 26
July 1992, 9:42 UTC (ERS-1, SLCI, orbit 5377, frame
2889, descending pass) off the coast of Malta. Fig.1
shows the quick-look of the SAR image. The ROIs,
which are manually selected in the SAR image, are
shown in Fig. 2 which is made by two sub-images
excerpted by Fig. 1. In the right and bottom-right side
of the image (see Figs. 1 and 2a) several oil slicks are
present. On the left side of the image (see Figs. 1 and
2b), near the coast of the isle of Malta, a low wind area
is present, in the top-middle of the image some ships
and small oil slicks are present. The ROIs relevant to
low-wind areas are labeled as “LW”, oil slicks are
labeled as “Oil” and the ROIs including the ships are
labeled as “S”. In Fig. 2c a zoom of the SAR image
relevant to Oil4 area is shown. The same format is used
in all subsequent experiments. The SAR image regards
a sea surface area characterized by a low-to-moderate
wind speed. In Tables 1 the GK parameters are listed
for all ROIs and corresponding reference areas. In all
cases the χ2-test of hypothesis with a 95% confidence
level was applied and passed.
Analysis of the three GK parameters shows that the
absolute value of η is useless, the absolute value of α is
of some interest and the absolute value of ν is useful.
Therefore the differential parameters η% and Δα have
been introduced. They are defined as follows:
⎛ η ROI − ηRef ⎞
⎟⎟ ⋅ 100 ,
⎝ η Ref
⎠
η% = ⎜⎜
(3)
and
Δα = α ROI - α Ref .
(4)
With respect to η, all these experiments show that the
dark areas are characterized by a negative η% value
generally greater than 50%. This is untrue for the Oil3
and LW2 ROIs, where η% value is negative but higher.
This can be physically explained by the sea-oil mixing
occurring in the Oil3 area and in general by the
heterogeneity of these two ROIs, see Figs. 1 and 2.
Physically, the characterization of dark areas in terms
of negative η% value is due to a smaller roughness in
dark areas. It is also important to note that the S1 and
S2 ROIs, characterized by the presence of a ship,
exhibit a very high positive η% value.
With respect to α it must be noted that the notation “∞”
is used to emphasize that a high value (>60) is
measured, since this means that a Gaussian scattering
regime is practically due. Results listed in Tables 1
clearly show that the dark areas are characterized by an
high negative Δα value witnessing a change into the
scattering regime. When the scattering regime changes
from the non-Gaussian to the Gaussian case Δα is equal
to -∞, when scattering regime changes from different
non-Gaussian cases Δα is finite and negative.
With respect to ν, results show that, as expected, it is
very low for all dark and reference areas. In S1 and S2
ROIs ν is much greater than usual values. To
emphasize this it is appropriate to consider ν% defined
analogously to (5). As result S1 and S2 ROIs are
characterized by very high and positive ν% values. It
must be noted however that, surprisingly, ν is not as
large as one would expect. In order to physically
explain such unexpected result a detailed analysis was
accomplished. Relevant pdfs (not shown to conserve
space) are indistinguishable from a K pdf, but for some
strong isolated outliers. These outliers witness the
presence of few strong dominating scatterers. As
matter of fact a Rice pdf in not achieved but the GK
parameters estimation procedure (section 2) fails to
estimate the α value because the measured pdf shows
some gaps and bins with very few number of samples
[17]. The notation “NA” is used to emphasize this. In
conclusion, the used GK parameters estimation
procedure is able to emphasize ROIs in which a
dominating scatterer is present.
The second case regards a well-known oil spill
accident which has been widely studied and
documented, e.g. [18]. The data set is relevant to the
acquisition of 02 June 2003, 21:02 UTC (ERS-2, SLCI,
orbit 42439, frame 1107, ascending pass). Fig.3 shows
the quick-look of the SAR image and the selected
ROIs. In this case all ROIs are relevant to oil slicks. It
is shown an oil slick of about 39 kmq offshore south of
the Swedish coast caused by the Fu Shan Hai bulk
carrier accident which occurred north of the Danish
island of Bornholm in the Baltic Sea, on Saturday 31
May. More than 100 tonnes of heavy fuel oil began
streaming from the ship hull, and currents carried the
oil towards Sweden [18]. The interest on this data set is
twofold: the dark area is relevant to a spillage 48 hours
old and the scene is characterized by a high wind speed
(8 m/s) [18]. Relevant results are shown in Table 2.
Physical results are in agreement to what formerly
experienced. However, it must be noted that the η%
values are greater than former one, see Table 1. This is
physically due to the oil spill aging process which is
not balanced by the high wind speed regime [19].
4. CONCLUSIONS
In this paper it has been shown that full-resolution
speckled SAR images can be effectively employed to
highlight the presence of low backscatter areas and
small strong scatterers. Classical approaches are not
able to process such SAR images and therefore deal
with multi-look SAR images where, at the expense of a
poorer spatial resolution, the speckle is mitigated. The
new approach is based on the GK model which is able
to embody very different scattering cases. Therefore, it
is possible to take advantage of such SAR fullresolution SAR images. Although this approach is not
able to discriminate among the physical causes
generating dark areas, the results demonstrates that GK
parameters are useful and that the analysis is simple
and effective. As a matter of fact, the approach can be
envisaged as an additional tool to assist classic oil spill
observation procedures.
ACKNOWLEDGMENTS
The authors acknowledge the ESA for providing the
SAR data (C1P-2769), the NASA (POODAC) for
providing the scatterometer wind data and the ECMWF
data centre for providing the wind data.
Figure 1. Quicklook image of the area of interest relevant to the acquisition of 26 July 1992, 9:42 UTC
(ERS-1, SLCI, orbit 5377, frame 2889, descending pass) off the coast of Malta.
(a)
(b)
Figure 2. Zooms of two part of SAR image of Fig.1.
Table 1.
Measured GK parameters relevant to ROIs of Figs. 2(a)- 2(b).
ROI
Oil 1
Ref. Sea
Oil 2
Ref. Sea
η
0.0009486
0.0008612
0.001968
Oil 3
0.001568
0.002083
Oil 4
0.001083
Ref. Sea
0.002085
Oil 5
0.001122
Ref. Sea
0.002194
Ref. Sea
Oil 7
Ref. Sea
LW 1
Ref. Sea
LW 2
Ref. Sea
S1
Ref. Sea
S2
Ref. Sea
-52.9
0.002014
Ref. Sea
Oil 6
η%
0.0007162
0.001520
0.0006750
0.001520
0.0002059
0.001093
0.0009361
0.001331
0.01387
0.001320
0.01225
0.001320
α
9
Δα
-11
20
-56.2
-24.7
-48.1
-48.9
-52.8
-55.6
-81.2
-29.7
+950
+828
9
31
13
∞
4
∞
35
∞
6
40
3
40
41
54
1
∞
NA
∞
NA
∞
ν
0.08
ν%
Wind
+14.2
Moderate
-33.3
Moderate
-60.0
Moderate
-20.0
Moderate
-57.1
Moderate
-72.7
Low
-81.8
Low
-42.8
Low
0.0
Low
+766
Low
+1400
Low
0.07
-22
-∞
-∞
-∞
-34
-37
-13
-∞
-
0.02
0.03
0.02
0.05
0.04
0.05
0.03
0.07
0.03
0.11
0.02
0.11
0.04
0.07
0.04
0.04
0.26
0.03
0.45
0.03
Figure 3. Quicklook image of the area of interest relevant to the acquisition of 02 June 2003, 21:02 UTC
(ERS-2, SLCI, orbit 42439, frame 1107, ascending pass) and sketch of the ROIs selected for parameters
estimation.
Table 2.
Measured GK parameters relevant to ROIs of Fig. 4
ROI
η
Oil 1
0.001074
Ref. Sea
0.001343
Oil 2
Ref. Sea
Oil 3a
Ref. Sea
Oil 3b
Ref. Sea
0.0008146
0.001321
0.0007391
0.001185
0.0009366
0.001185
η%
-20.1
α
5
2.
3.
4.
5.
6.
7.
8.
9.
-33
38
-38.3
-37.7
-21.0
10
20
15
33
17
33
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