Parameter Selection Criteria for Tomo-SAR Focusing

Martin-del-Campo-Becerra, Gustavo Daniel; Serafín-García, Sergio Alejandro; Reigber, Andreas; Ortega-Cisneros, Susana

doi:10.1109/jstars.2020.3042661

Cited by 11 publications

(9 citation statements)

References 31 publications

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“…We consider a L-band SAR sensor (0.23 m wavelength) at a nominal altitude of 3000 m. The acquisition geometry consists of 7 evenly distributed passes (flight tracks) spanning a PLOS synthetic aperture (see Fig. 2) of 60 m. For a slant-range distance from the targets to the master track of about 4000 m, the attained Fourier resolution [12] is approximately 7.5 m.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…according to the Bayes formula and since ln{•} is a monotonically increasing function. Setting 𝑝(𝐛) ≈ 𝑐𝑜𝑛𝑠𝑡, since it is unknown, and ignoring those terms that do not comprise 𝐛 in (12), the log-likelihood function is defined as…”

Section: Model Order Selectionmentioning

confidence: 99%

“…In order to select the regularization parameter 𝜉 = 𝑁 , the L-Curve method is employed. Detailed in [12], the L-Curve method seeks a balance between the norm of a penalty term and the norm of the residual. Basically, it consists in forming a smooth curve by plotting the points…”

Section: Ml-inspired Adaptive Robust Iterative Approachmentioning

confidence: 99%

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Resolution Enhancement of Spatial Parametric Methods via Regularization

Martin-del-Campo-Becerra

Serafín-García

Reigber

et al. 2021

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

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The spatial spectral estimation problem has applications in a variety of fields, including radar, telecommunications, and biomedical engineering. Among the different approaches for estimating the spatial spectral pattern, there are several parametric methods, as the well-known multiple signal classification (MUSIC). Parametric methods like MUSIC are reduced to the problem of selecting an integer-valued parameter [so-called model order (MO)], which describes the number of signals impinging on the sensors array. Commonly, the best MO corresponds to the actual number of targets, nonetheless, relatively large model orders also retrieve good-fitted responses when the data generating mechanism is more complex than the models used to fit it. Most commonly employed MO selection (MOS) tools are based on information theoretic criteria [e.g., Akaike information criterion (AIC), minimum description length (MDL) and efficient detection criterion (EDC)]. Normally, the implementation of these tools involves the eigenvalues decomposition of the data covariance matrix. A major drawback of such parametric methods (together with certain MOS tool) is the drastic accuracy decrease in adverse scenarios, particularly, with low signal-tonoise ratio, since the separation of the signal and noise sub-spaces becomes more difficult to achieve. Consequently, with the aim of refining the responses attained by parametric techniques like MUSIC, this article suggests utilizing regularization as a postprocessing step. Furthermore, as an alternative, this work also explores the possibility of selecting a single relatively large MO (rather than using MOS tools) and enhancing via regularization, the solutions retrieved by the treated parametric methods. In order to demonstrate the capabilities of this novel strategy, synthetic aperture radar (SAR) tomography (TomoSAR) is considered as application.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Model Order Selectionmentioning

confidence: 99%

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Resolution Enhancement of Spatial Parametric Methods via Regularization

Martin-del-Campo-Becerra

Serafín-García

Reigber

et al. 2021

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

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“…Locating the corner is the most significant issue for Lhypersurface method. Variety of methods exists on locating the corner of a single-parameter L-curve [18], [24], [29]- [31]. As the number of parameters increases, however, the complexity and uncertainty in locating the corner on the L-hypersurface also escalate, which leads to challenges including the definition of corner, the loss of monotonicity between residual and penalty terms, the intricate patterns of L-hypersurface.…”

Section: Determination Of Cornermentioning

confidence: 99%

“…In all application scenarios, regularization parameters control the effect of corresponding penalty terms and can greatly influence the performance of regularization models. Past studies have provided us a series of parameter selection methods for regularization models with single penalty term, such as the L-curve [16]- [18], Stein's unbiased risk estimate (SURE) [17]- [20], generalized crossvalidation (GCV) [17], [18], [20]- [22], etc. However, the existing multi-parameter selection methods summarized by [23] mainly deal with penalty terms in semi-norm(∥Γ x∥ p p ) form, little attention had been paid to parameters selection for composite regularization models with arbitrary penalty terms in recent years.…”

Section: Introductionmentioning

confidence: 99%

L-Hypersurface Based Parameters Selection in Composite Regularization Models With Application to SAR and TomoSAR Imaging

Fan,

Wang,

et al. 2023

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

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Composite regularization models are widely used in sparse signal processing, making multiple regularization parameters selection a significant problem to be solved. Variety kinds of composite regularization models are used in sparse microwave imaging, including ℓ1 and ℓ2 penalty, nonconvex and TV penalty, combined dictionary, etc. In this article, a new adaptive multiple regularization parameters selection method named L-hypersurface is proposed. The effectiveness of the proposed method is verified by experiments. Simulation experiments indicate that the selected optimal regularization parameters have satisfied reconstruction results, both visually and numerically. Furthermore, experiments on Gaofen-3 SAR satellite data are also exploited to show the performance of the proposed method.

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