Parisar: Patch-Based Estimation and Regularized Inversion for Multibaseline SAR Interferometry

Ferraioli, Giampaolo; Deledalle, Charles-Alban; Denis, Loïc; Tupin, Florence

doi:10.1109/tgrs.2017.2765761

Cited by 31 publications

(18 citation statements)

References 46 publications

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“…Nevertheless, these results are very promising and confirm the strong potential of the proposed -Net for the generation of high-resolution DEMs. As a future investigation, we also aim at comparing the generated DEM products by using different unwrapping approaches, as the PUMA algorithm [56], and by using recent methodologies that embed interferometric phase estimation and unwrapping in a single approach, as for the PARISAR algorithm [57].…”

Section: B Analysis On High-resolution Dem Generationmentioning

confidence: 99%

Φ-Net: Deep Residual Learning for InSAR Parameters Estimation

Sica

Gobbi

Rizzoli

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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Nowadays, deep learning (DL) finds application in a large number of scientific fields, among which the estimation and the enhancement of signals disrupted by the noise of different natures. In this article, we address the problem of the estimation of the interferometric parameters from synthetic aperture radar (SAR) data. In particular, we combine convolutional neural networks together with the concept of residual learning to define a novel architecture, named-Net, for the joint estimation of the interferometric phase and coherence.-Net is trained using synthetic data obtained by an innovative strategy based on the theoretical modeling of the physics behind the SAR acquisition principle. This strategy allows the network to generalize the estimation problem with respect to: 1) different noise levels; 2) the nature of the imaged target on the ground; and 3) the acquisition geometry. We then analyze the-Net performance on an independent data set of synthesized interferometric data, as well as on real InSAR data from the TanDEM-X and Sentinel-1 missions. The proposed architecture provides better results with respect to state-of-the-art InSAR algorithms on both synthetic and real test data. Finally, we perform an application-oriented study on the retrieval of the topographic information, which shows that-Net is a strong candidate for the generation of high-quality digital elevation models at a resolution close to the one of the original single-look complex data.

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Section: B Analysis On High-resolution Dem Generationmentioning

confidence: 99%

Φ-Net: Deep Residual Learning for InSAR Parameters Estimation

Sica

Gobbi

Rizzoli

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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“…In [26], the non-local approach is combined with a regularization model (minimization of the Total Variation) taking into account the spatially variant speckle reduction.…”

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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“…Using this approach, and by making some simple considerations the function of Eq. (4) can be simplified into an easier expression, providing the Patchbased estimation and regularized inversion for multi-baseline SAR interferometry (PARISAR) estimator proposed in [11].…”

Section: Map-nl Estimatormentioning

confidence: 99%

The Exploitation of the Non Local Paradigm for SAR 3d Reconstruction

Ferraioli

Dénis

Deledalle

et al. 2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

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In the last decades, several approaches for solving the Phase Unwrapping (PhU) problem using multi-channel Interferometric Synthetic Aperture Radar (InSAR) data have been developed. Many of the proposed approaches are based on statistical estimation theory, both classical and Bayesian. In particular, the statistical approaches based on the use of the whole complex multi-channel dataset have turned to be effective. The latter are based on the exploitation of the covariance matrix, which contains the parameters of interest. In this paper, the added value of the Non Local (NL) paradigm within the InSAR multi-channel PhU framework is investigated. The analysis of the impact of NL technique is performed using multi-channel realistic simulated data and X-band data.

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Parisar: Patch-Based Estimation and Regularized Inversion for Multibaseline SAR Interferometry

Cited by 31 publications

References 46 publications

Φ-Net: Deep Residual Learning for InSAR Parameters Estimation

Φ-Net: Deep Residual Learning for InSAR Parameters Estimation

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

The Exploitation of the Non Local Paradigm for SAR 3d Reconstruction

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