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Estimation of normalized coherency matrix through the SIRV model. Application to high resolution POLSAR data

Gabriel Vasile(1), Jean-Philippe Ovarlez(2), Frederic Pascal(3) and Michel Gay(1)

(1) GIPSA-lab, CNRS, 961 rue de la Houille Blanche, F-38402 Saint Martin d'Heres cedex, France
(2) ONERA, Chemin de la Huniere, 91761 Palaiseau Cedex, France
(3) SONDRA, Plateau du Moulon, 3 rue Joliot-Curie, 91192 Gif-sur-Yvette Cedex, France

Abstract

In the context of non-Gaussian polarimetric clutter models, this paper presents an application of the recent advances in the field of Spherically Invariant Random Vectors (SIRV) modelling for coherency matrix estimation in heterogeneous clutter. The complete description of the POLSAR data set is achieved by estimating the span and the normalized coherency independently [1]. The normalized coherency describes the polarimetric diversity, while the span indicates the total received power. The main advantages of the proposed Fixed Point estimator [2] are that it does not require any ''a priori'' information about the probability density function of the texture (or span) and it can be directly applied on adaptive neighbourhoods. Interesting results are obtained when coupling this Fixed Point estimator with an adaptive spatial support based on the scalar span information. Based on the SIRV model, a new maximum likelihood distance measure is introduced for unsupervised POLSAR classification. The proposed method is tested with both simulated POLSAR data and airborne POLSAR images provided by the RAMSES system. Results of entropy/alpha/anisotropy decomposition, followed by unsupervised classification, allow discussing the use of the normalized coherency and the span as two separate descriptors of POLSAR data sets.

[1] Vasile G., Ovarlez J.-P., Pascal F., Tison C., Bombrun L., Gay M., Trouvé E., Normalized coherency matrix estimation under the SIRV model - Alpine glacier POLSAR data analysis, IEEE International Geoscience and Remote Sensing Symposium, Boston, USA, July 2008, 4 pages [2] Pascal F., Chitour Y., Ovarlez J.-P., Forster P. and Larzabal P., Covariance structure maximum-likelihood estimates in compound Gaussian noise: existence and algorithm analysis, IEEE Transactions on Signal Processing, Vol. 56, No. 1, 2008, pp. 34-48

 

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  Higher level                 Last modified: 07.05.06