The Role of Nonlocal Estimation in SAR Tomographic Imaging of Volumetric Media

Aghababaee, Hossein; Ferraioli, Giampaolo; Schirinzi, Gilda; Sahebi, Mahmod Reza

doi:10.1109/lgrs.2018.2808681

Cited by 20 publications

(11 citation statements)

References 13 publications

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“…Consequently, in order to achieve high multilooking, preventing the spatial mixture of sources, rather than using a Boxcar filter, we recommend the use of nonlocal spatially adaptive filtering methods, which enhance the estimation of the covariance matrices, improving the scatterer separation in layover areas thanks to their smoothing and edge-preserving properties. However, this topic is out of the scope of this article, but the reader may refer to [36] and [37].…”

Section: Resultsmentioning

confidence: 99%

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Statistical Regularization for Enhanced TomoSAR Imaging

Campo

Nannini

Reigber

2020

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

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One of the main topics in synthetic aperture radar (SAR) tomography (TomoSAR) is the estimation of the vertical structures' location, which scatter the field back toward the sensor, constrained to a reduced number of passes. Moreover, the introduction of artifacts and the increase in the ambiguity levels due to irregular sampling, consequence of nonuniform acquisition constellations, complicate the accurate estimation of the source parameters. Pursuing the alleviation of such drawbacks, the use of statistical regularization approaches, based on the maximum-likelihood estimation theory, has been successfully demonstrated in the previous related studies. However, these techniques are constrained to the assumption that the probability density function of the observed data is Gaussian. In this article, in order to solve the ill-posed nonlinear TomoSAR inverse problem, we relax this assumption and apply the weighted covariance fitting (WCF) criterion instead. The latter alleviates the previously mentioned drawbacks and retrieves a power spectrum pattern with an outline more similar to the expected one, i.e., recovered using matched spatial filtering with a higher number of tracks. First, we present the mathematical background of the related regularization methods, adapted to solve the TomoSAR inverse problem, from which we derive our novel technique, named WCF-based iterative spectral estimator (WISE). Then, the differences and similarities between the addressed regularization approaches are discussed, besides their main advantages and disadvantages. Finally, the implementation details of WISE are treated, along with simulated examples and experimental results obtained from a forested test site. Index Terms-Maximum-likelihood (ML), power spectrum pattern (PSP), spectral analysis (SA), synthetic aperture radar (SAR) tomography (TomoSAR), weighted covariance fitting (WCF). NOMENCLATURE A. List of Acronyms BMR Bayes minimum risk. CLS Constrained least squares. CS Compressed sensing. DOA Direction of arrival. ESPRIT Estimation of signal parameters via rotational invariance techniques. IAA-ML Iterative adaptive approach for ML. LS Least squares.

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Section: Resultsmentioning

confidence: 99%

“…If we relax the Gaussian assumption in (32), then the estimates given after solving (37) are no longer ML estimates. In such a case, it makes more sense to use a covariance fitting criterion.…”

Section: Wcf Criterionmentioning

confidence: 99%

Statistical Regularization for Enhanced TomoSAR Imaging

Campo

Nannini

Reigber

2020

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

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“…Therefore, tomographic filtering is often reduced to interferometric filtering between pairs of images. Examples of the use of patch-based approaches for SAR tomography can be found in [27], [28] and [29].…”

Section: Interferometric and Tomographic Datamentioning

confidence: 99%

Ten Years of Patch-Based Approaches for Sar Imaging: A Review

Tupin¹,

Dénis²,

Deledalle³

et al. 2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

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Speckle reduction is a major issue for many SAR imaging applications using amplitude, interferometric, polarimetric or tomographic data. This subject has been widely investigated using various approaches. Since a decade, breakthrough methods based on patches have brought unprecedented results to improve the estimation of radar properties. In this paper, we give a review of the different adaptations which have been proposed in the past years for different SAR modalities (mono-channel data like intensity images, multi-channel data like interferometric, tomographic or polarimetric data, or multimodalities combining optic and SAR images), and discuss the new trends on this subject.

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“…Extraction of physical features of forest structure is important in the monitoring and modeling of the dynamics of the ecosystem. Synthetic aperture radar (SAR) tomography (TomoSAR) with resolution capability in the elevation directions is a multi-dimensional signal processing technique that has great value for the estimation of forest structures [1,2]. Generally, forest environment is considered as two-layer media along the height dimension, in which the medium is characterized by random distributed volumetric scattering over the ground [3].…”

Section: Introductionmentioning

confidence: 99%

Sparsity Based Full Rank Polarimetric Reconstruction of Coherence Matrix T

et al. 2019

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In the frame of polarimetric synthetic aperture radar (SAR) tomography, full-ranks reconstruction framework has been recognized as a significant technique for fully characterization of superimposed scatterers in a resolution cell. The technique, mainly is characterized by the advantages of polarimetric scattering pattern reconstruction, allows physical feature extraction of the scatterers. In this paper, to overcome the limitations of conventional full-rank tomographic techniques in natural environments, a polarimetric estimator with advantages of super-resolution imaging is proposed. Under the frame of compressive sensing (CS) and sparsity based reconstruction, the profile of second order polarimetric coherence matrix T is recovered. Once the polarimetric coherence matrices of the scatterers are available, the physical features can be extracted using classical polarimetric processing techniques. The objective of this study is to evaluate the performance of the proposed full-rank polarimetric reconstruction by means of conventional three-component decomposition of T, and focusing on the consistency of vertical resolution and polarimetric scattering pattern of the scatterers. The outcomes from simulated and two different real data sets confirm that significant improvement can be achieved in the reconstruction quality with respect to conventional approaches.

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The Role of Nonlocal Estimation in SAR Tomographic Imaging of Volumetric Media

Cited by 20 publications

References 13 publications

Statistical Regularization for Enhanced TomoSAR Imaging

Statistical Regularization for Enhanced TomoSAR Imaging

Ten Years of Patch-Based Approaches for Sar Imaging: A Review

Sparsity Based Full Rank Polarimetric Reconstruction of Coherence Matrix T

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