Abstract
Synthetic aperture radar (SAR) tomography (TomoSAR) is a multibaseline interferometric technique that estimates the power spectrum pattern (PSP) along the perpendicular to the line-of-sight (PLOS) direction. TomoSAR achieves the separation of individual scatterers in layover areas, allowing for the 3D representation of urban zones. These scenes are typically characterized by buildings of different heights, with layover between the facades of the higher structures, the rooftop of the smaller edifices and the ground surface. Multilooking, as required by most spectral estimation techniques, reduces the azimuth-range spatial resolution, since it is accomplished through the averaging of adjacent values, e.g., via Boxcar filtering. Consequently, with the aim of avoiding the spatial mixture of sources due to multilooking, this article proposes a novel methodology to perform single-look TomoSAR over urban areas. First, a robust version of Capon is applied to focus the TomoSAR data, being robust against the rank-deficiencies of the data covariance matrices. Afterward, the recovered PSP is refined using statistical regularization, attaining resolution enhancement, suppression of artifacts and reduction of the ambiguity levels. The capabilities of the proposed methodology are demonstrated by means of strip-map airborne data of the Jet Propulsion Laboratory (JPL) and the National Aeronautics and Space Administration (NASA), acquired by the uninhabited aerial vehicle SAR (UAVSAR) system over the urban area of Munich, Germany in 2015. Making use of multipolarization data [horizontal/horizontal (HH), horizontal/vertical (HV) and vertical/vertical (VV)], a comparative analysis against popular focusing techniques for urban monitoring (i.e., matched filtering, Capon and compressive sensing (CS)) is addressed.
Highlights
Synthetic aperture radar (SAR) tomography (TomoSAR) is a multibaseline interferometric technique, whose main goal is the estimation of the power spectrum pattern (PSP) along the perpendicular to the line-of-sight (PLOS) direction [1,2]
The rest of the paper is organized in the following manner: the TomoSAR signal model is reviewed in Section 2; Section 3 studies a modified version of the WISE iterative statistical regularization approach, using DCRCB to retrieve its first input; Section 4 is dedicated to present the experimental results, performed using strip-map airborne uninhabited aerial vehicle SAR (UAVSAR) data; lastly, Section 5 presents a discussion on the previously given experimental results, providing the concluding remarks
For MSF and DCRCB, the set of tomograms depicting the wings of the selected edifice (Figures 11–13) present high ambiguity levels among the PLOS height range from −35 m to 60 m and from −10 m to −20 m, with a pseudo-power up to −3 dB and −5 dB, respectively, in the VV polarization
Summary
Synthetic aperture radar (SAR) tomography (TomoSAR) is a multibaseline interferometric technique, whose main goal is the estimation of the power spectrum pattern (PSP) along the perpendicular to the line-of-sight (PLOS) direction [1,2]. Applying Capon beamforming for focusing implicates multilooking on the set of data covariance matrices, which reduces the azimuth-range spatial resolution. The implementation of these techniques, introduced in [12], involves the use of Capon beamforming to retrieve such a first estimate, which serves as first input (zero-step iteration) to the corresponding iterative procedures The latter entails the mixture of sources as a consequence of spatial multilooking, since Capon requires guaranteeing the data covariance matrices to be invertible. Apart of preserving the range/azimuth spatial resolution, this set-up is useful for the study of the several point-type targets, situated in urban scenes, in this paper, the proposed methodology is pushed to its limits by taking into account the single-look case to perform TomoSAR focusing. The rest of the paper is organized in the following manner: the TomoSAR signal model is reviewed in Section 2; Section 3 studies a modified version of the WISE iterative statistical regularization approach, using DCRCB to retrieve its first input (zero-step iteration); Section 4 is dedicated to present the experimental results, performed using strip-map airborne UAVSAR data; lastly, Section 5 presents a discussion on the previously given experimental results, providing the concluding remarks
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