Single-Look SAR Tomography of Urban Areas

Martín-del-Campo-Becerra, Gustavo Daniel; Reigber, Andreas; Nannini, Matteo; Hensley, S.

doi:10.3390/rs12162555

Cited by 8 publications

(8 citation statements)

References 27 publications

(59 reference statements)

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“…This section makes use of real data provided by the Jet Propulsion Laboratory (JPL) and the National Aeronautics and Space Administration (NASA). The dataset gathers L-band fully-polarimetric TomoSAR data collections from Munich (the third largest city in Germany), acquired by the UAVSAR system in 2015 [10], [13], [24]. The aircraft (Gulfstream G-III) flew at a nominal altitude of 12.5 km with a swath of 22 km and length of 60 km.…”

Section: Resultsmentioning

confidence: 99%

“…In order to express (13) as required by (9), the modelled data covariance matrix 𝐑 𝐲 in (6) must be represented as function of 𝛗(𝑛) instead of 𝐛, as described by [8, Eq. 9]…”

Section: Model Order Selectionmentioning

confidence: 99%

“…In previous related studies [10] - [13], [22], [23], different statistical regularization techniques are extended to cope with nonlinear ill-posed inverse problems, which is the case of TomoSAR. Among them, we find MARIA, an iterative statistical regularization method that performs resolution enhancement, suppression of artifacts and reduction of ambiguity levels [11].…”

Section: Ml-inspired Adaptive Robust Iterative Approachmentioning

confidence: 99%

“…One of the main advantages of regularization approaches as MARIA is their flexibility; in the sense that they can have as input, in principle, the retrievals of any parametric or non-parametric focusing technique. Previous related studies [10] - [13], utilize non-parametric methods as input (e.g., MSF, Capon beamforming, DCRCB, etc.). Yet, the usage of parametric techniques like MUSIC for such a purpose had not been studied yet.…”

mentioning

confidence: 99%

“…Its main goal is estimating the locations of the vertical structures that scatter the field back towards the (active) sensor. The TomoSAR inverse problem is typically defined via the linear EO [10] - [13]…”

mentioning

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: Resultsmentioning

confidence: 99%

“…In order to express (13) as required by (9), the modelled data covariance matrix 𝐑 𝐲 in (6) must be represented as function of 𝛗(𝑛) instead of 𝐛, as described by [8, Eq. 9]…”

Section: Model Order Selectionmentioning

confidence: 99%