The Yellow River is the most sediment-filled river and the sixth-longest one in the world. It is of paramount importance for safe navigation, local economy and environment due to the presence of floods, farms, aquacultures and pollution. Further, the Yellow River delta is characterized by several physical phenomena due to both natural and anthropogenic processes: sedimentation, erosion, floods, pollution, etc. In this study, actual partially-overlapped L-/C-band fullypolarimetric (FP) synthetic aperture radar (SAR) data are used to investigate the scattering properties of the Yellow River delta, whose very complex area is characterized by different environments as recorded by ground truth data acquired during a ship-based in-situ campaign. Preliminary results witness that multi-polarization and multi-frequency SAR measurements allow inferring more physical information in such a complex environment that can be used as starting point for developing ad hoc classification algorithms.

Multi-frequency and multi-polarization study on SAR-based coastal areas characterization: The case of the yellowriver delta

BUONO, ANDREA;NUNZIATA, FERDINANDO;MIGLIACCIO, Maurizio;
2016-01-01

Abstract

The Yellow River is the most sediment-filled river and the sixth-longest one in the world. It is of paramount importance for safe navigation, local economy and environment due to the presence of floods, farms, aquacultures and pollution. Further, the Yellow River delta is characterized by several physical phenomena due to both natural and anthropogenic processes: sedimentation, erosion, floods, pollution, etc. In this study, actual partially-overlapped L-/C-band fullypolarimetric (FP) synthetic aperture radar (SAR) data are used to investigate the scattering properties of the Yellow River delta, whose very complex area is characterized by different environments as recorded by ground truth data acquired during a ship-based in-situ campaign. Preliminary results witness that multi-polarization and multi-frequency SAR measurements allow inferring more physical information in such a complex environment that can be used as starting point for developing ad hoc classification algorithms.
2016
9789292213053
9789292213053
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11367/54219
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