Two polarimetric segmentation techniques have been
devised and implemented by the authors, starting from the
generalised maximum likelihood approach with a Wishart
distribution: a region growing approach (POL MUM) and
global likelihood approach (POLSEGANN) based on the
simulated annealing. Both techniques exploit the properties
of the covariance matrix of the data, but they proceed with
very different approaches to identify the widest possible
homogeneous segments. A number of different sets of
simulated images with known segments and polarimetric
characteristics is used to compare the segmentation
performance of the different techniques. Different measures
are introduced to compare the achieved segments, among
which the probability of correct classification and the
fractional overlap between segments that belong to the same
region. The comparative performance of the two techniques
on a set of SIRC polarimetric images are also discussed.
The extraction of information from
polarimetric SAR images, that carry appreciably more
information about the observed scene than the
single-channel SAR images, is still an area of active
research for the many operational polarimetric SAR that are
becoming available in the very near future. In particular,
an interesting approach to the extraction of information
consists in applying a segmentation to the SAR images
before any further operation of classification or feature
extraction. In fact, it has been clearly demonstrated (see
, ) that large performance improvement can be
achieved by first segmenting the SAR image into regions
with homogeneous characteristics, and then classifying the
resulting global regions. It is therefore of interest to
define optimal segmentation techniques for the polarimetric
SAR images. Many previous works were related more with the
exploitation of both the reflectivity and the phase
relationship between the different polarimetric channels to
provide useful indications on the characteristics of the
different observed objects, allowing a more accurate
classification [1-4]. Many examples of both segmentation
and terrain classification using polarimetric SAR data can
be easily found in literature, with application to very
different monitoring and surveillance applications [5-10].
The theory at the basis of effective approaches for the
segmentation of polarimetric SAR images has been proposed
by the authors in , . These works presented the
derivation of the generalised likelihood function of a
single segment of polarimetric SAR image. This was shown to
be the basic step to obtain both the global generalised
likelihood of the whole polarimetric SAR image and the
split-merge test between two adjacent regions. Then, the
global generalised likelihood was used in connection with a
simulated annealing approach to derive a polarimetric
segmentation scheme, named POLSEGANN. The aim of this paper
is to introduce a region growing technique (POL MUM) for
segmenting the polarimetric SAR images, based on the
optimal split-merge test. The resulting algorithm is then
compared to POLSEGANN to assess their comparative
theoretical performance both on simulated data and real SAR
data. A full set of cases is studied to generate a complete
simulated comparison of the techniques. A specific test
pattern is selected containing many regions with different
shapes and randomly filled with samples from seven
different classes. A number of random images are generate
in a Monte Carlo simulation with the same statistical
properties in each region. Then both POLSEGANN and POLMUM
are fed with the same data and the accuracy of the
segmentation is evaluated, by averaging the performance
over the set of simulations. Different measures are
introduced to compare the achieved segments: (i) the
probability of correct classification; (ii) the fractional
overlap between segments that belong to the same region.
Both measures are based on the a priori knowledge on the
generated images, that can be used to evaluate the
behaviour of the different techniques. In the first case
(i), the average confusion matrix is generated, that allows
us to discuss the ability to discriminate between adjacent
regions belonging to different classes with similar
properties. The average probability of correct
classification yields a global measure of segmentation
correctness, since a bad segmentation also implies a bad
classification. However, this is a global measure and it is
not very sensitive to the shape of the regions borders. The
second measure is selected to analyse the border estimation
accuracy and is based on a differential measure of overlap.
Both techniques show to operate effectively against the
simulated pattern with a little advantage for the POLSEGANN
technique in the extraction of the shape of the borders and
dealing with the small regions. Moreover, the comparative
performance are evaluated in terms of computational
complexity. Finally, the proposed segmentation techniques
are applied to a set of polarimetric SIR-C images. The
segmentation performance against the real data are compared
in terms of correct classification using the available
 Oliver, C.J., and Quegan, S.,
"Understanding SAR images", Artech House, NY, 1998.
Zyl, J.J., Zebker, H.A., Elachi, C., "Imaging radar
polarization signatures: Theory and observations", Radio
Science, Vol. 22, 1987, pp. 529-543.
 Ulaby, F.T. and
Elachi, C., Radar Polarimetry for Geoscience Applications,
Artech House, Norwood, 1990.
 Zebker, H.A., Van Zyl,
J.J., "Imaging radar polarimetry: A review", Proceedings
IEEE, Vol. 79, 1991, pp. 1583-1606.
 Novak, L. M. and
Burl, M. C., "Optimal speckle reduction in polarimetric SAR
imagery", IEEE Trans. Aerospace Electronic Systems, Vol.
26, 1990, pp. 293-305.
 Lee, J., Grunes, M. R. and
Mango, S. A., "Speckle reduction in multipolarisation,
multifrequency SAR imagery", IEEE Trans. Geoscience and
Remote Sensing, Vol. 29, 1991, pp. 535-544.
P., Oliver C.J., "Optimum Detection and Segmentation of
oil-slicks using polarimetric SAR data", IEE Proceedings on
Radar, Sonar and Navigation, Vol. 147, No. 6, December
 Cloude, S.R., Pottier, E., "A Review of Target
Decomposition Theorems in Radar Polarimetry", IEEE Trans.
on GRS, Vol. 34, 1996, pp. 498-518.
 Cloude, S. R. and
Pottier, E., "An entropy based classification scheme for
land applications of polarimetric SAR", IEEE Trans.
Geoscience and Remote Sensing, Vol. 35, 1997, pp. 68-78.
 Lee, J.-S. and Hoppel, K. W., "Principal components
transformation of multifrequency polarimetric SAR imagery",
IEEE Trans. Geoscience and Remote Sensing, Vol. 30, 1992,
 T. Macrž Pellizzeri, C. J. Oliver, P.
Lombardo, "Segmentation-Based Joint Classification of SAR
and Optical Images", in print IEE Proceedings on Radar
Sonar and Navigation, 2002.
 P. Lombardo, C.J. Oliver,
"Optimal Classification of Polarimetric SAR images Using
Segmentation", IEEE Radar Conference 2002, Long Beach (CA),
 P. Lombardo, C.J. Oliver, "Optimal
Polarimetric Segmentation for the Classification of
Agricultural Areas", EUSAR 2002, Koeln, Germany, June 2002.