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 arithmetical mean and present a physical interpretation for these improvements, using the Cloude-Pottier decomposition.
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The authors would like to thank the DGA for funding this research.
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Formont, P., Ovarlez, JP., Pascal, F. (2013). On the Use of Matrix Information Geometry for Polarimetric SAR Image Classification. In: Nielsen, F., Bhatia, R. (eds) Matrix Information Geometry. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30232-9_10
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DOI: https://doi.org/10.1007/978-3-642-30232-9_10
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