International audienceThis paper deals with seabed imaging issued from sonar systems. Such imaging systems produce images of backscattering (BS) strength relative to physical seabed characteristics. However, these Bs measurements are not only seabed-related but also dependent on the incident angle. Therefore, to enhance the quality of such seabed imaging systems, we develop an unsupervised approach to compensate for these seabed-related angular dependencies. Our approach combines robust estimation and hidden Markov random fields. Results on real data demonstrate the relevance of our approach to improve seabed observation

Augustin, Jean-Marie

Fablet, Ronan

English

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Unsupervised calibrated sonar imaging for seabed
observation using hidden Markov random fields
Ronan Fablet, Jean-Marie Augustin
To cite this version:
Ronan Fablet, Jean-Marie Augustin. Unsupervised calibrated sonar imaging for seabed observation us-
ing hidden Markov random fields. ICASSP 2006 : IEEE International Conference on Acoustics, Speech
and Signal Processing, May 2006, Toulouse, France. pp.693 - 696, ￿10.1109/ICASSP.2006.1660468￿.
￿hal-02344294￿
UNSUPERVISED CALIBRATED SONAR IMAGING FOR SEABED OBSERVATION USING
HIDDEN MARKOV RANDOM FIELDS
R. Fablet and J.-M. Augustin
Ifremer, TSI-STH
Technople Brest Iroise, 29280 PLOUZANE, France
ABSTRACT
This paper aims at enhancing seabed imaging issued from
sonar systems. Such imaging systems produce maps of back-
scattering strength relative to physical seabed characteristics.
However, these maps show a dependency on the incident an-
gles. This dependency is in addition seabed-related. To en-
hance the quality of these imaging systems, we develop an un-
supervised approach to compensate for these seabed-related
angular dependencies. Our approach combines robust esti-
mation and hidden Markov random fields. Results on real BS
images demonstrate the relevance of our approach to improve
seabed observation.
1. PROBLEM STATEMENT AND RELATED WORK
Sonar imaging provides a remote sensing tool to observe and
characterize the physical properties of the seafloor and is in-
creasingly exploited for a variety of applications such as en-
vironmental monitoring, marine geosciences and biology, as
well as oil industry or defense. The current challenge for
swath sonars from imaging systems lies in the calibration of
reliable instruments for reflectivity measurement. This is re-
quirement both to make feasible the comparison of seafloor
properties at different locations along a single observation
path as well as the monitoring of the changes of seafloor prop-
erties along time.
This issue comes from the intrinsic characteristics of sonar
imaging systems. Since these systems rely on acoustic reflec-
tion of echoes emitted by the system, reflectivity measure-
ment of the seafloor obviously depends on the incident angle.
Consequently, similar seafloor types viewed from different
incident angles do not produce similar reflectivity measure-
ment. Sonar calibration is then a major task to compensate for
this angular dependencies. This issue is indeed enhanced by
the fact that these dependencies are seafloor-related. Hence,
the calibration issue is hardly linked to seafloor-based seg-
mentation.
This paper tackles this calibration issue and exploit ro-
bust estimation of backscattering models and hidden Markov
models to state this issue within a Bayesian framework. The
paper is organized as follows. Section 2 deals with Backscat-
ter modelling and estimation, and Section 3 presents the pro-
posed unsupervised approach for sonar calibration. Experi-
mental results are reported in Section 4.
2. BACKSCATTER MODELLING AND ESTIMATION
2.1. Characteristics of acoustic signals recorded by sonar
systems
Signals recorded by seafloor-mapping swath sonars poten-
tially provide an absolute measurement of the seafloor backscat-
tering strength as a function of incident angle. However the
measured echo level is not only dependent on the seafloor
backscattering strength, but is actually affected by phenom-
ena bound to the measurement configuration (transmission
range and angle), to seawater properties (absorption, refrac-
tion) and to the sonar itself (array directivity patterns, receiver
processing). These various factors may be summarized in the
classical sonar equation [5], expressed here in dB, summariz-
ing the building of the echo level as a function of time:
EL(t) = SL+Dt[θa(t)]− 2TL[R(t)] +BS[θb(t)]
+ 10log(S[R(t), θb(t)]) +Dr[θa(t)] + PG(t)
(1)
EL(t) is the electrical signal (in dB re.1V) corresponding to
the echo level, as a function of reception time t. SL is the
source level (dB re.1Pa@1m), modulated by the transmission
directivity pattern Dt expressed as a function of angle at the
sonar arrays θa(t). R(t) is the range/time relation, and TL the
Transmission loss. The backscattering strength BS, seafloor
dependent, also strongly depends on the signal frequency and
moreover on the incident angle θb(t) on the seafloor. Scat-
tering area S is bound both to the sonar parameters (beam
aperture, signal duration) and the range/angle/time relations.
Dr is the array directivity patterns on reception. Finally, PG
is the sonar receiver processing gain featuring both constant
terms (linked to various stages of pre-amplification and filter-
ing) and a time variation.
Among these different components of the received echo
level, all except the backscattering strengthBS are mainly re-
lated to the characteristics of the exploited sonar system. Pre-
vious work [1, 4] have proposed post-processing steps to deal
Fig. 1. Illustration of the characteristics of BS images for seabed imaging: from left to right, BS image involving two seafloor
types; associated map of incident angles; plot of the BS measure as a function of the incident angles for both seaflor types.
with these components. Fig.1 displays an example of such a
post-processed BS image. This image conveys meaningful in-
formation about the physical properties of the seafloor. How-
ever, as noted previously and stressed by Fig.1, this measure
is dependent on the incident angle. More precisely, BS val-
ues are characterized by a specular component (high values)
for incident angles close to 0 degree and decrease when the
incident angle increases. Consequently, the same seafloor re-
gion viewed at different incident angles does not resort to the
same BS map. The need to compensating for this bias is ob-
vious. It is however a difficult task since the relation between
the BS value and the incident angle is seafloor dependent as
illustrated by Fig.1.
2.2. BS modeling
A variety of models have been proposed [5] to model for a
given seafloor the evolution of the mean BS measure as a
function of the incident angle. In this work, we rely on a
trade-off between the complexity of the model and its ability
to account for a wide range of seafloor configurations. Fol-
lowing [5], the considered model is stated as the sum of a
specular component and of a generalized Lambert’s law:
BSΨ(θ) = 10log10
(
10−A exp(−Bθ2) + 10−C cosD(θ)) (2)
In the subsequent, Ψ will denote the set of model parameters
(A,B,C,D).
2.3. Robust sequential estimation
Let us assume that we are given with a set of BS values {BSi}i
associated with incident angles {θi}i. We further assume that
τSi is the likelihood that the BS measure BSi is relative to a
given seafloor type S. Hence, the estimation of the BS model
ΨS for seafloor type S is stated as the minimization of the
following robust criterion:
Ψ̂S = arg min
ΨS
∑
i
τSi · ρ [BSi −BSΨS (θi)] (3)
rho is a robust function, such as the Leclerc estimator [3]:
ρ(r) = 1 − exp(−r2/σ2R), where σ is a scale parameter.
Equivalently, this robust minimization is solved for using an
iterated least-square procedure. This procedure iterates two
steps: 1) the computations of robust weights for the current
estimate ΨS :
ωi = φ(BSi −BSΨS (θi)) (4)
where φ is the influence function φ(r) = ρ′(r)/r; 2) the min-
imization of the following quadratic criterion for the updated
robust weights ωii:
Ψ̂S = arg min
ΨS
∑
i
τSi · ωi · [BSi −BSΨS (θi)]2 (5)
Rather than directly solving for this quadratic criterion us-
ing a gradient-based or incremental approach, we adopt a se-
quential scheme. It comes to alternately updating specular
and Lambert components. More precisely, for given parame-
ters of the specular component, the parameters of the Lambert
component are updated as the solution of the following crite-
rion:
Ψ̂L,S = arg min
ΨL,S
∑
i
τSi ·ωi ·
[
B˜S
spec
i −BSΨL,S (θi)
]2
(6)
where ΨL,S is the BS model issued from the Lambert com-
ponent of model ΨS , B˜S
spec
i the residual when compensat-
ing for the current estimate of the specular component. Since
model ΨL,S is linear w.r.t. parameters C and D, the up-
dated model parameters Ψ̂L,S are exactly computed as a least-
square solution:
Ψ̂L,S =
[∑
i
τSiωiZi
TZi
]−1∑
i
τSiωiB˜S
spec
i Zi (7)
where Zi = [1 θ2i . We proceed in a similar way to update
the parameters of the specular component given the Lambert
component. We iterate this robust sequential estimation until
convergence.
To improve the convergence properties of this robust esti-
mation scheme, scale parameter σR is set to 1.57∗med({ri}),
where {ri} are the residuals relative to the initial least-square
parameter estimate. This scale parameter is hen decreased
according to a geometrical law.
3. BAYESIAN COMPENSATION FOR
SEABED-RELATED ANGULAR DEPENDENCIES
We aim at removing the effects of the incident angles in the
measure of the backscattering strength. Since these effects
are seabed-related, this compensation can be regarded as a
segmentation issue w.r.t. seabed types. More precisely, given
a set of seabed types {S1, ..., Sn}, our goal is to estimate the
posterior likelihood of the map of the seabed types given the
observed BS image. Let us denote by x the map of the seabed
types and BS the map of BS measures associated with the
map of incident angles θ, we aim at evaluating the following
posterior likelihood for any pixel p and seabed type S:
τp,S = P (xp = S|BS; θ;xq, q 6= p) (8)
Given these posterior likelihoods, the compensated BS image
can be estimated as:
BScomp(p) = BSp −
∑
k
τp,Sk
(
BSΨSk (θp)− CSk
)
(9)
where
∑
k τp,Sk
(
BSΨSk (θp)− CSk
)
stands for the expecta-
tion of the angular dependency, when the reference BS level
is set to the value of Lambert’s component at incident angle
0.
3.1. Markovian setting and Gibbs sampling
In order to evaluate posterior likelihoods τp,S , we resort to a
Markovian setting [2]. Assuming that x is a Markov random
field with a 4-neighborhood structure and that BS measures
BSp are independent conditionally to xp, τp,S can be written
as:
τp,S ∝ P (BSp|xp = S, θp)P (xp = S|xq, q 6= p) (10)
Data-drivenP (BSp|xp = S, θp) term is evaluated as the Gaus-
sian likelihood of residual BSp −BSΨSk (θp):
P (BSp|xp = S, θp) =
exp
(
− (BSp −BSΨS )
2
2σ2S(θp)
)
√
2piσ2S(θp)
(11)
where σS(θp) is the angle-dependent standard deviation of
the Gaussian error model for seabed type S. Let us stress that
the data-driven model involves an angle-dependent standard
deviation, since it has been observed experimentally that the
distribution of the residual errors vary over angles.
The a priori Markovian model set on x comes to:
P (xp|xq, q 6= p) = P (xp|xq, q ∈ Vp)
∝ exp
−γ ∑
q∈Vp
δ(xp − xq)
 (12)
where Vp are the four neighbors of pixel p, δ the Dirac func-
tion and γ the regularization coefficient which balances the
relative influences of the regularization term and of the data-
driven term.
The overall model is then parametrized by the set of seabed
BS models {ΨS , σS} and the regularization weight γ. Given
these parameters, posterior likelihoods τp,S are estimated us-
ing the Gibbs sampler [2]. From an initial label map x0, it
comes to generate a sequence of maps {xk}. At iteration k,
xk is first initialized to xk−1. Then, for each pixel p, seabed
label xk(p) is randomly selected within {S1, ...., SN} accord-
ing to the likelihoods P
(
xkp = S|BS; θ; {xkq , q ∈ Vp}
)
given
by Eq. 10. From the sequence of maps {xk}, posterior τp,S
is estimated as an empirical average:
τp,S =
#{k ∈ [N1, N2], xk(p) = S}
N2−N1 + 1 (13)
where #A is the cardinality of set A. N2 is the length of the
sequence generated by the Gibbs sampler and N1 the number
of iterations needed to reach the stationarity.
3.2. Unsupervised compensation
The compensation scheme defined by Eq. 9 as well as the
Gibbs sampler require to explicitly define the BS models. As-
suming that the number of seabed types is known, the com-
pensation is then an unsupervised issue where we need to
jointly achieve the estimation of the parameters of the BS
models and the compensation. To solve for this unsupervised
issue, we exploit an ICE (Iterative Conditional Estimation)
procedure [6]. At iteration k, this procedure involves two
steps:
• step 1 : For current model parameters {ΨkS , σkS}, pos-
teriors τkp,S are estimated using the Gibbs sampler (cf.
Eq.13). Note that, as initialization, we provide the last
map of the Gibbs sequence generated at iteration k−1.
• step 2 : Model parameters {ΨkS , σkS} are updated from
current posterior estimates τ kp,S as stated by Eq.7. Fol-
lowing a non-parametric approach, standard deviation
models σkS are updated as weighted averages of the cur-
rent residuals, with weights computed as the products
of the posteriors and a Gaussian angular kernel to ac-
count for angular dependencies.
In order to achieve a better convergence of the Gibbs sam-
pler, we adopt a multiresolution strategy. From a Gaussian
pyramid of the Initial BS and angular maps, we iterate the
ICE procedure from the coarsest resolution to the finest one.
The initialization of the Gibbs sampler at each resolution is
issued from the last seabed map generated at the previous res-
olution.
At the coarsest resolution, the initial model parameters
{Ψ0S , σ0S} are given by the EM-based estimation of the pa-
rameter of a mixture models of BS models. In addition, to
Fig. 2. Example of unsupervised BS (Backsctarreing Strength) compensation: from left to right, original BS map involving two
seafloor types, segmentation map issued from the estimated posterior likelihood, compensated BS image.
increase the robustness of the proposed scheme, the first ICE
steps are carried out with simpler BS models accounting only
Lambert’s components. The specular components are intro-
duced after a predefined number of iterations.
At final iteration and finest resolution, the compensated
BS map is finally computed from Eq. 9 using the final poste-
riors τKp,S .
4. EXPERIMENTS
We have carried out experiments with real BS observations
acquired with a multibeam sonar system. We report in Fig.
2 an example including the original images, the segmentation
map given by x̂p = arg maxS τp,S and the compensated BS
map. This two example shows how the proposed unsuper-
vised compensation scheme can improve the visualization of
seafloor characteristics. In both cases, the specular compo-
nent is clearly identified by the very bright vertical zone cor-
responding to low incident angles, whereas regions on both
sides of the image (i.e., relative to greater incident angles)
tend to be darker. The reported segmentation map illustrates
that the proposed unsupervised scheme succeeds in identify-
ing the meaningful seafloor models. The estimated BS mod-
els are displayed in Fig.3. The compensated BS image per-
mits to better visualize and compare the reflectivity measures
within and between each seafloor region.
−1.5 −1 −0.5 0 0.5 1 1.5
−50
−40
−30
−20
−10
0
incident angle
BS
 m
ea
su
re
Fig. 3. Estimated BS models for the sonar image displayed in
Fig.2: each color refers to one model with the associated BS
data.
Original image Compensated image
Fig. 4. Comparison of the original BS map and of the com-
pensated one for two subregions of the example reported in
Fig. 2.
The zoom on two regions of the BS map, displayed in
Fig;4 further stresses the enhancement brought by our scheme.
As an additional relevant feature, it also removes artifacts, ap-
pearing as horizontal lines, which are due variations of the
incident angles along the path of the sonar systems caused
by pitch and roll. Since the proposed compensation produces
calibrated reflectivity measures, these observation artifacts are
removed.
5. REFERENCES
[1] J. M. Augustin and X. Lurton. Image amplitude calibration and process-
ing for seafloor mapping sonars. In Proc. of IEEE on Ocean, Oceans’05,
Brest, France, June 2005.
[2] S. Geman and D. Geman. Stochastic relaxation, Gibbs distribution and
the Bayesian restoration of images. IEEE Trans. on Pattern Analysis and
Machine Intelligence, 6(6):721–741, 1984.
[3] P. Huber. Robust Statistics. John Wiley & Sons, 1981.
[4] X.Lurton L.Hellequin, J.M.Boucher. Processing of high-frequency
multibeam echo sounder data for seafloor characterization. IEEE Jour-
nal of Oceanic Engineering, 28(1):78–89, 2003.
[5] X. Lurton. An introduction to underwater acoustics. Springer, 2002.
[6] F. Salzenstein and W. Pieczynski. Parameter estimation in hidden fuzzy
markov random fields and image segmentation. Graph. Models: Image
Process., 59(4):205–220, 1997.


Unsupervised calibrated sonar imaging for seabed observation using hidden Markov random fields

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Unsupervised calibrated sonar imaging for seabed observation using hidden Markov random fields

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