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Model-based segmentation techniques for multifrequency polarimetric SAR

Prof. Pierfrancesco Lombardo (1), Tiziana Macrž Pellizzeri(1) , Marco Meloni(1) , and Massimo Sciotti(1)

(1) University of Rome 'La Sapienza', Via Eudossiana 18, 00184 - Rome, Italy


A new technique, named DPOL MUM, is proposed for the segmentation of multifrequency polarimetric SAR images, that exploits the characteristic block diagonal structure of their covariance matrix. This technique is based on the newly introduced split-merge test, that has a reduced fluctuation error than the straight extension of the polarimetric test (POL MUM) and is shown to yield a more accurate segmentation on simulated SAR images. DPOL-MUM is especially useful in the extraction of information from urban areas that are characterised by the presence of different spectral and polarimetric characteristics. Its effectiveness is demonstrated by applying it to segment a set of polarimetric SIR-C images of the town of Pavia. The classification of the image segmented with DPOL MUM shows higher probability of correct classification compared to POL MUM and to a similar technique that does not use the correlation properties (MT MUM).

Extended summary

It is well known that multifrequency polarimetric SAR images carry appreciably more information about the observed scene than the single-channel SAR images. In particular, the specific reflectivity at the different frequencies and both the reflectivity and the phase relationships between the different polarimetric channels 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]. Many proposed techniques operating with polarimetric SAR images are suited to pixel-by- pixel classification on the basis of the polarimetric properties, whereas it has been clearly demonstrated (see [11], [1]) that large performance improvement can be achieved by first segmenting the 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 multifrequency polarimetric SAR images. Moreover, it has been shown that the use of multifrequency polarimetric SAR images yields a further improvement in the possibility to identify and classify homogeneous regions with slightly different terrain characteristics. An effective approach for the segmentation of polarimetric SAR images has been proposed in [12], [13], which can easily been extended to perform the joint segmentation of any set of complex correlated SAR images and therefore also to the case of multifrequency polarimetric images. However, the corresponding large increase of dimensionality implies also an increased level of uncertainty in the underlying test, that is a potential source of performance degradation. However, it is usual to model the images acquired at different frequencies as uncorrelated. This is also the case for many other situations, as the case of multitemporal set of images collected with long revisit time, [14]. The exploitation of this model, that corresponds to a block diagonal covariance matrix for the multifrequency polarimetric images can largely reduce the uncertainty in the segmentation test and yield much better performance than the simple extension of the polarimetric segmentation scheme. Therefore, the aim of this paper is to derive optimal segmentation techniques that fully exploit the statistical model of the multifrequency polarimetric SAR images, and to evaluate their performance both theoretically and on simulated data. The application to a set of SAR images of urban areas shows the practical results obtained with the proposed technique. The proposed techniques use a generalised Maximum Likelihood (ML) approach, based on the joint Probability Density Function (PDF) of the pixels in each homogeneous region. In particular, we adopt a multivariate Gaussian model to describe the statistical behaviour of the single channels (and therefore a complex Wishart model for the distribution of the corresponding multilook complex data), thus the statistical characteristics of the different regions are encoded in the covariance matrix. The proposed segmentation techniques are based on the use of an optimal split-merge test to merge regions with similar characteristics (region fusion), and they are totally unsupervised. The generalized likelihood ratio test is used to derive the optimal test to decide whether two adjacent regions should be split or merged. The likelihood function is also the basis of the proposed supervised classification technique. Based on the results in [15], we extend to the general multifrequency case the derivation of the ML segmentation technique presented in [12], [13], which makes no assumption on the structure of the covariance matrix and is particularly suited for monofrequency, monotemporal polarimetric images. Thereafter, we consider the special case of a covariance matrix with a special, known structure that can be used as a priori information to reduce the losses introduced by its local estimation performed inside the algorithm. In particular, we consider the case of a block diagonal structure for the covariance matrix, that is usually the case for multifrequency or multitemporal polarimetric SAR data. Thus, we derive a ML segmentation technique specifically designed for this case. A theoretical performance analysis of the optimum model based Split-Merge Test is presented. The analysis is used to set the appropriate thresholds for the test as a function of the desired false alarm probability and the regions size. Simulation is then adopted to confirm these predictions. Moreover, the two proposed approaches are compared, showing that the use of the a priori information about the covariance matrix structure results in an increased capability to discriminate regions with different characteristics. Finally, the proposed segmentation techniques are applied to a set of polarimetric and multifrequency (C and L band ) SIR-C images of the town of Pavia, in Northern Italy.


[1] Oliver, C.J., and Quegan, S., "Understanding SAR images", Artech House, NY, 1998.

[2] Van Zyl, J.J., Zebker, H.A., Elachi, C., "Imaging radar polarization signatures: Theory and observations", Radio Science, Vol. 22, 1987, pp. 529-543.

[3] Ulaby, F.T. and Elachi, C., Radar Polarimetry for Geoscience Applications, Artech House, Norwood, 1990.

[4] Zebker, H.A., Van Zyl, J.J., "Imaging radar polarimetry: A review", Proceedings IEEE, Vol. 79, 1991, pp. 1583-1606.

[5] 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.

[6] 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.

[7] Novak, L. M., Burl, M. C. and Irving, W. W., "Optimal polarimetric processing for enhanced target detection", IEEE Trans. Aerospace Electronic Systems, Vol. 29, 1993, pp. 234-244.

[8] Cloude, S.R., Pottier, E., "A Review of Target Decomposition Theorems in Radar Polarimetry", IEEE Trans. on GRS, Vol. 34, 1996, pp. 498-518.

[9] 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.

[10] Lee, J.-S. and Hoppel, K. W., "Principal components transformation of multifrequency polarimetric SAR imagery", IEEE Trans. Geoscience and Remote Sensing, Vol. 30, 1992, pp. 686-696.

[11] 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.

[12] P. Lombardo, C.J. Oliver, "Optimal Classification of Polarimetric SAR images Using Segmentation", IEEE Radar Conference 2002, Long Beach (CA), April 2002.

[13] P. Lombardo, C.J. Oliver, "Optimal Polarimetric Segmentation for the Classification of Agricultural Areas", EUSAR 2002, Koeln, Germany, June 2002.

[14]P. Lombardo, T. Macrž Pellizzeri, "Maximum Likelihood Signal Processing Techniques to Detect a Step Pattern of Change in Multitemporal SAR Images", IEEE Transactions on Geoscience and Remote Sensing, Vol. 40, No. 4, April 2002, pp. 853-870.

[15] Lombardo 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 2000.


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