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Estimation and segmentation in non-Gaussian POLSAR clutter by SIRV stochastic processes

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 co-herency independently. The normalized coherency describes the polarimetric diversity, while the span indicates the total received power. Based on the SIRV model, a new maximum likelihood distance measure is introduced for unsupervised POLSAR segmentation. The proposed method is tested with airborne POLSAR images provided by the RAMSES system.
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Submitted on : Thursday, February 27, 2020 - 7:03:00 PM
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Gabriel Vasile, Jean-Philippe Ovarlez, Frédéric Pascal. Estimation and segmentation in non-Gaussian POLSAR clutter by SIRV stochastic processes. 2009 IEEE International Geoscience and Remote Sensing Symposium, Jul 2009, Cape Town, South Africa. pp.III-963-III-966, ⟨10.1109/IGARSS.2009.5417935⟩. ⟨hal-02493508⟩

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