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Remote Sens. 2019, 11(2), 132;

Urban Tomographic Imaging Using Polarimetric SAR Data

Dipartimento di Ingegneria, Università Degli Studi di Napoli “Parthenope”, 80133 Napoli NA, Italy
Author to whom correspondence should be addressed.
Received: 30 November 2018 / Revised: 27 December 2018 / Accepted: 8 January 2019 / Published: 11 January 2019
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In this paper, we investigate the potential of polarimetric Synthetic Aperture Radar (SAR) tomography (Pol-TomoSAR) in urban applications. TomoSAR exploits the amplitude and phase of the received data and offers the possibility to resolve multiple scatters lying in the same range–azimuth resolution cell. In urban environments, this issue is very important since layover causes multiple coherent scatterers to be mapped in the same range–azimuth image pixel. To achieve reliable and accurate results, TomoSAR requires a large number of multi-baseline acquisitions which, for satellite-borne SAR systems, are collected with long time intervals. Then, accurate tomographic reconstructions would require multiple scatterers to remain stable between all the acquisitions. In this paper, an extension of a generalized likelihood ratio test (GLRT)-based tomographic approach, denoted as Fast-Sup-GLRT, to the polarimetric data case is introduced, with the purpose of investigating if, in urban applications, the use of polarimetric channels allows for reduction of the number of baselines required to achieve a given scatterer’s detection performance. The results presented show that the use of dual polarization data allows the proposed detector to work in an equivalent or better way than use of a double number of independent single polarization channels. View Full-Text
Keywords: synthetic aperture radar; tomography; polarimetry; radar detection; generalized likelihood ratio test; sparse signals synthetic aperture radar; tomography; polarimetry; radar detection; generalized likelihood ratio test; sparse signals

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Budillon, A.; Johnsy, A.C.; Schirinzi, G. Urban Tomographic Imaging Using Polarimetric SAR Data. Remote Sens. 2019, 11, 132.

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