An electromagnetic model, based on the generalized-K (GK) speckle distribution, has been developed to read the scattering features associated with metallic objects at sea in full resolution single-look complex (SLC) synthetic aperture radar (SAR) data. The sensitivity of the GK parameters is investigated in both copolarized and cross-polarized SAR data. It is shown that a proper combination of GK parameters exhibits a completely different behavior with respect to sea surface with and without metallic objects, when measured over cross-polarized SAR data. As a matter of fact, a simple and very effective approach to observe metallic objects in full resolution SLC cross-polarized SAR data, is proposed. Experiments undertaken over a large data set consisting of RADARSAT-2 SLC fine quad polarization SAR data, confirm model predictions, and show that the proposed approach is both physically-based and operationally effective, since it is able to provide a logical true and false output.
Generalized-K (GK)-Based Observation of Metallic Objects at Sea in Full-Resolution Synthetic Aperture Radar (SAR) Data: A Multipolarization Study
FERRARA, Giuseppe;MIGLIACCIO, Maurizio;NUNZIATA, FERDINANDO;SORRENTINO, Antonio
2011-01-01
Abstract
An electromagnetic model, based on the generalized-K (GK) speckle distribution, has been developed to read the scattering features associated with metallic objects at sea in full resolution single-look complex (SLC) synthetic aperture radar (SAR) data. The sensitivity of the GK parameters is investigated in both copolarized and cross-polarized SAR data. It is shown that a proper combination of GK parameters exhibits a completely different behavior with respect to sea surface with and without metallic objects, when measured over cross-polarized SAR data. As a matter of fact, a simple and very effective approach to observe metallic objects in full resolution SLC cross-polarized SAR data, is proposed. Experiments undertaken over a large data set consisting of RADARSAT-2 SLC fine quad polarization SAR data, confirm model predictions, and show that the proposed approach is both physically-based and operationally effective, since it is able to provide a logical true and false output.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
