The Beltrami SAR Framework for Multichannel Despeckling

Amao-Oliva, Joel; Torres-Roman, D.; Yañez-Vargas, Israel; Reigber, Andreas; Jäger, Marc

doi:10.1109/jstars.2019.2917086

Cited by 5 publications

(6 citation statements)

References 27 publications

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“…Finally, future lines of research will focus on using better methods to perform multilooking rather than the standard Boxcar filtering. The use of nonlocal spatially adaptive filtering methods is a good option, which enhances the estimation of the covariance matrices, improving the scatterer separation in layover areas thanks to their smoothing and edge-preserving properties [30,31]. Another line of research is the use of ML to estimate the particular structures of the data covariance matrices, making them closer, in a statistical sense, to the true covariance matrices.…”

Section: Discussionmentioning

confidence: 99%

Single-Look SAR Tomography of Urban Areas

Martín-del-Campo-Becerra

Reigber

Nannini

et al. 2020

Remote Sensing

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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.

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Section: Discussionmentioning

confidence: 99%

Single-Look SAR Tomography of Urban Areas

Martín-del-Campo-Becerra

Reigber

Nannini

et al. 2020

Remote Sensing

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“…where i  is the first derivative with respect to slow time. Using the bisection method, the numerical root of ( (11) In (11),  conj  denotes the conjugated operation. After this step, the signal is compressed in range.…”

Section: Phase Error With Short Integration Timementioning

confidence: 99%

“…Besides, the bistatic feature of echoed signal at reference range is completely cancelled. However, targets not located at reference range suffer from residual errors, which are determined by the phase difference between ( 5) and (11).…”

Section: Phase Error With Short Integration Timementioning

confidence: 99%

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Multireceiver SAS Imagery Based on Monostatic Conversion

Zhang

Sun

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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To use monostatic based imaging algorithms for multireceiver synthetic aperture sonar, the monostatic conversion is often carried out based on phase centre approximation, which is widely exploited by multireceiver SAS systems. This paper presents a novel aspect for dealing with the multireceiver SAS imagery, which still depends on the idea of monostatic conversion. The approach in this paper is based on Loffeld's bistatic formula that consists of two important terms, i.e., quasi monostatic and bistatic deformation terms. Our basic idea is to preprocess the bistatic deformation term and then incorporate the quasi monostatic term into an analogous monostatic spectrum. With this new spectrum, traditional imaging algorithms designed for monostatic synthetic aperture sonar can be easily exploited. In this paper, we show that Loffeld's bistatic formula can be reduced to the same formula as spectrum based on phase centre approximation when certain conditions are met. Based on our error analysis, the maximum error magnitude of PCA method is about 1 rad, which would noticeably affect the SAS imagery. Fortunately, the error magnitude of presented method can be always kept within π 4 . It means that Loffeld's bistatic formula provides a more accurate approximation of the spectrum compared to that based on phase centre approximation. After that, this paper develops a new imaging scheme and presents imaging results. Based on quantitative comparisons, the presented method well focuses multireceiver SAS data, and it provides better image compared to phase centre approximation method.

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“…That is to say, the high resolution in the azimuth dimension is usually obtained at the cost of mapping swath or vice versa. Fortunately, the multireceiver SAS [9][10][11] well solves this issue. This technique exploits a receiver array rather than a single receiver, and the receiver array is deployed in the azimuth dimension.…”

Section: Introductionmentioning

confidence: 99%

Efficient imaging method for multireceiver SAS

Zhang

Yang²,

Feng

et al. 2022

IET Radar Sonar & Navi

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With the Loffeld's bistatic formula, the point target reference spectrum (PTRS) of multireceiver synthetic aperture sonar (SAS) can be decomposed into the quasi monostatic term and bistatic deformation (BD) term. The former term is analogous to the monostatic SAS PTRS while the latter one describes the bistatic phase introduced by the bistatic displacement between the transmitter and receiver. The multireceiver SAS echo signal can be transformed into monostatic SAS equivalent data after correcting the BD term. Based on the range sub-block processing method, this data transformation is conducted by using the preprocessing step. Then, fast imaging processors based on monostatic SAS system can be directly exploited to process the monostatic SAS equivalent data. In this paper, the authors mainly focus on the application of non-linear chirp scaling algorithm. At last, the simulations and real data are used to validate the presented method. The processing results show that the imaging performance of presented method is nearly close to that of traditional method. Besides, the imaging efficiency is highly improved compared to the phase centre approximation (PCA) method.

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The Beltrami SAR Framework for Multichannel Despeckling

Cited by 5 publications

References 27 publications

Single-Look SAR Tomography of Urban Areas

Single-Look SAR Tomography of Urban Areas

Multireceiver SAS Imagery Based on Monostatic Conversion

Efficient imaging method for multireceiver SAS

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