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A Test statistic for high resolution polarimetric SAR Data classification

Abstract : Modern SAR systems have high resolution which leads the backscattering clutter to be non-Gaussian. In order to properly classify images from these systems, a non-Gaussian noise model is considered: the SIRV model. A statistical test of equality of covariance matrices is used to classify pixels, taking into account the critical region of the test which rejects the likeliness of a covariance matrix to any of the class centers. This test is applied on experimental data obtained with the ONERA RAMSES system in X-band. The results show a good separation between natural and man-made areas of the image.
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Submitted on : Friday, June 4, 2021 - 8:56:15 PM
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Pierre Formont, Jean-Philippe Ovarlez, Frédéric Pascal, Gabriel Vasile, Laurent Ferro-Famil. A Test statistic for high resolution polarimetric SAR Data classification. IGARSS 2010 - IEEE International Geoscience and Remote Sensing Symposium, Jul 2010, Honolulu, Hawaii, United States. ⟨10.1109/IGARSS.2010.5651074⟩. ⟨hal-00507077⟩



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