$Point Target Classification via Fast Lossless and Sufficient \Omega- \Psi - \Phi Invariant Decomposition of High-Resolution and Fully Polarimetric SAR/ISAR Data$

Point Target Classification via Fast Lossless and Sufficient \Omega- \Psi - \Phi Invariant Decomposition of High-Resolution and Fully Polarimetric SAR/ISAR Data

The classification of high-resolution and fully polarimetric SAR/ISAR data has gained a lot of attention in remote sensing and surveillance problems and is addressed by decomposing the radar target Sinclair matrix. In this paper, the Sinclair matrix has been projected onto the circular polarization...

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Veröffentlicht in:	Proceedings of the IEEE 2013-03, Vol.101 (3), p.798-830
Hauptverfasser:	Paladini, Riccardo, Ferro Famil, Laurent, Pottier, Eric, Martorella, Marco, Berizzi, Fabrizio, Dalle Mese, Enzo
Format:	Artikel
Sprache:	eng
Schlagworte:	Automatic target classification automatic target recognition Circular polarization Classification classification algorithm Classification algorithms data mining Decomposition decomposition theorem depolarization effect deterministic processes Earth Observing System Earth surface eigenvalues and eigenfunctions Einstein photon circular polarization Faraday rotation geophysics computing invariant decomposition Invariants Lossless lunar surface Mathematical analysis Matrix decomposition Moon orientation invariant parameters particle characterization of radio scattering theory polarimetry Polarization polarization transformation properties radar Radar antennas radar cross section (RCS) radar polarimetry Radar scattering radio scattering models Remote sensing remote sensing by radar Sinclair matrix Surface treatment Surveillance Synthetic aperture radar target decomposition target scattering characterization Target tracking vectors
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