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Extension of a Fast GLRT Algorithm to 5D SAR Tomography of Urban Areas

Dipartimento di Ingegneria, Università degli Studi di Napoli “Parthenope”, Naples 80143, Italy
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Academic Editors: Salvatore Stramond and Prasad S. Thenkabail
Remote Sens. 2017, 9(8), 844; https://doi.org/10.3390/rs9080844
Received: 12 June 2017 / Revised: 26 July 2017 / Accepted: 4 August 2017 / Published: 14 August 2017
This paper analyzes a method for Synthetic Aperture Radar (SAR) Tomographic (TomoSAR) imaging, allowing the detection of multiple scatterers that can exhibit time deformation and thermal dilation by using a CFAR (Constant False Alarm Rate) approach. In the last decade, several methods for TomoSAR have been proposed. The objective of this paper is to present the results obtained on high resolution tomographic SAR data of urban areas, by using a statistical test for detecting multiple scatterers that takes into account phase variations due to possible deformations and/or thermal dilation. The test can be evaluated in terms of probability of detection (PD) and probability of false alarm (PFA), and is based on an approximation of a Generalized Likelihood Ratio Test (GLRT), denoted as Fast-Sup-GLRT. It was already applied and validated by the authors in the 3D case, while here it is extended and experimented in the 5D case. Numerical experiments on simulated and on StripMap TerraSAR-X (TSX) data have been carried out. The presented results show that the adopted method allows the detection of a large number of scatterers and the estimation of their position with a good accuracy, and that the consideration of the thermal dilation and surface deformation helps in recovering more single and double scatterers, with respect to the case in which these contributions are not taken into account. Moreover, the capability of method to provide reliable estimates of the deformations in urban structure suggests its use in structure stress monitoring. View Full-Text
Keywords: synthetic aperture radar; tomography; radar detection; generalized likelihood ratio test; sparse signals synthetic aperture radar; tomography; radar detection; generalized likelihood ratio test; sparse signals
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MDPI and ACS Style

Budillon, A.; Johnsy, A.C.; Schirinzi, G. Extension of a Fast GLRT Algorithm to 5D SAR Tomography of Urban Areas. Remote Sens. 2017, 9, 844. https://doi.org/10.3390/rs9080844

AMA Style

Budillon A, Johnsy AC, Schirinzi G. Extension of a Fast GLRT Algorithm to 5D SAR Tomography of Urban Areas. Remote Sensing. 2017; 9(8):844. https://doi.org/10.3390/rs9080844

Chicago/Turabian Style

Budillon, Alessandra, Angel Caroline Johnsy, and Gilda Schirinzi. 2017. "Extension of a Fast GLRT Algorithm to 5D SAR Tomography of Urban Areas" Remote Sensing 9, no. 8: 844. https://doi.org/10.3390/rs9080844

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