In this study, multi-polarization synthetic aperture radar (SAR) data collected by the C-band Radarsat-2 SAR mission in Antarctica are exploited to observe the Terra Nova Bay (TNB). The latter represent a complex coastal scenario where open ocean, sea ice, polynyas, ice sheets and tongues are present. Single-polarization (SP) features, including co- and cross-polarized backscattering intensities, and dual-polarimetric (DP) features, including eigenvalue-based decomposition features and the polarization ratio, are used to identify the signatures of the different environments that characterize the TNB and to perform an unsupervised classification. Experimental results, obtained using Markov Random Fields and polarimetric decompositions, show that: i) SP and DP features allow estimating the extent of polynya; ii) DP approach allows inferring useful information on the different scenarios; iii) clear signatures can be identified according to the considered multi-polarization feature set.

Multi-polarization SAR measurements to observe coastal areas in Antarctica

Nunziata F.;Buono A.;Migliaccio M.
2018-01-01

Abstract

In this study, multi-polarization synthetic aperture radar (SAR) data collected by the C-band Radarsat-2 SAR mission in Antarctica are exploited to observe the Terra Nova Bay (TNB). The latter represent a complex coastal scenario where open ocean, sea ice, polynyas, ice sheets and tongues are present. Single-polarization (SP) features, including co- and cross-polarized backscattering intensities, and dual-polarimetric (DP) features, including eigenvalue-based decomposition features and the polarization ratio, are used to identify the signatures of the different environments that characterize the TNB and to perform an unsupervised classification. Experimental results, obtained using Markov Random Fields and polarimetric decompositions, show that: i) SP and DP features allow estimating the extent of polynya; ii) DP approach allows inferring useful information on the different scenarios; iii) clear signatures can be identified according to the considered multi-polarization feature set.
2018
978-1-5386-7150-4
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11367/77258
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