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Supervised classification by neural networks using polarimetric time-frequency signatures

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6 Author(s)
Duquenoy, M. ; French Aerosp. Lab., DEMR/TSI, Palaiseau, France ; Ovarlez, J.P. ; Morisseau, C. ; Vieillard, G.
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In radar imaging, the assumption is made that scatterers are white in the emitted frequency band and isotropic for all direction of observation. Nevertheless, new capacities in radar imaging, using a wideband and a large angular excursion, make these hypotheses not valid. Time-frequency analysis highlight this point of view and show some scatterers are anisotropic and/or dispersive. This information source can be completed by radar polarimetry. This paper suggests a supervised classification of scatterers using neural networks based on polarimetric time-frequency signatures. This method is applied here on anechoic chamber data, however can be generalized to SAR or circular SAR imaging.

Published in:

Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009  (Volume:4 )

Date of Conference:

12-17 July 2009

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