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On the use of Matrix Information Geometry for Polarimetric SAR Image Classification

Abstract : Polarimetric SAR images have a large number of applications. To extract a physical interpretation of such images, a classification on their polarimetric properties can be a real advantage. However, most classification techniques are developed under a Gaussian assumption of the signal and compute cluster centers using the standard arithmetical mean. This paper will present classification results on simulated and real images using a non-Gaussian signal model, more adapted to the high resolution images and a geometrical definition of the mean for the computation of the class centers. We will show notable improvements on the classification results with the geometrical mean over the arithmeti-cal mean and present a physical interpretation for these improvements, using the Cloude-Pottier decomposition.
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Pierre Formont, Jean-Philippe Ovarlez, Frédéric Pascal. On the use of Matrix Information Geometry for Polarimetric SAR Image Classification. Matrix Information Geometry, Springer Berlin Heidelberg, pp.257-276, 2013, ⟨10.1007/978-3-642-30232-9_10⟩. ⟨hal-02494996⟩

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