First 3-D Reconstructions of Targets Hidden Beneath Foliage by Means of Polarimetric SAR Tomography

Nannini, Matteo; Scheiber, Rolf; Horn, Ralf; Moreira, Alberto

doi:10.1109/lgrs.2011.2160329

Cited by 59 publications

(52 citation statements)

References 16 publications

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“…However, it achieves limited vertical resolution and is prone to ambiguities if the number of baselines is low and/or they are irregularly distributed. With better vertical resolution capabilities, Capon beamforming is currently the standard algorithm most widely employed in the tomographic SAR community [28][29][30]. Aiming to achieve finer vertical resolutions, algorithms based on Compressive Sensing (CS) techniques have been proposed to solve the underdetermined system in Equation (6), and have been successfully applied to urban [31,32] and forest scenarios [33].…”

Section: Tomographic Sar Imagingmentioning

confidence: 99%

“…where S is a vector with the heights at which at least one peak is found in the unit structure window; R is the length of S; and S represents the mean of vector S. For example, if in a given area the heights in meters of the peaks are [30,25,25,10,10,10,8,2] then S = [30,25,10,8] and R = 4. Finally, the vertical descriptor is normalized by its maximum within the image (or within the set of images that are going to be compared).…”

Section: Forest Structure Estimationmentioning

confidence: 99%

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Monitoring of Forest Structure Dynamics by Means of L-Band SAR Tomography

et al. 2017

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Synthetic Aperture Radar Tomography (TomoSAR) allows the reconstruction of the 3D reflectivity of natural volume scatterers such as forests, thus providing an opportunity to infer structure information in 3D. In this paper, the potential of TomoSAR data at L-band to monitor temporal variations of forest structure is addressed using simulated and experimental datasets. First, 3D reflectivity profiles were extracted by means of TomoSAR reconstruction based on a Compressive Sensing (CS) approach. Next, two complementary indices for the description of horizontal and vertical forest structure were defined and estimated by means of the distribution of local maxima of the reconstructed reflectivity profiles. To assess the sensitivity and consistency of the proposed methodology, variations of these indices for different types of forest changes in simulated as well as in real scenarios were analyzed and assessed against different sources of reference data: airborne Lidar measurements, high resolution optical images, and forest inventory data. The forest structure maps obtained indicated the potential to distinguish between different forest stages and the identification of different types of forest structure changes induced by logging, natural disturbance, or forest management.

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Section: Tomographic Sar Imagingmentioning

confidence: 99%

Section: Forest Structure Estimationmentioning

confidence: 99%

Monitoring of Forest Structure Dynamics by Means of L-Band SAR Tomography

et al. 2017

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“…The L 2;1 norm not only promotes sparsity along rows and minimizes the energy along columns, but also exponentially reduces the probability of recovery failure in the number of columns ofP. 21 The distribution of the backscattered power over forested areas is not sparse in the object domain 19,[22][23][24] because at least the Fourier spectrum of canopy backscatter is spread along the cross-range direction. Thus, a sparse basis needs to be found.…”

Section: Fully Polarimetric Wavelet-based Distributed Compressive Senmentioning

confidence: 99%

Multibaseline polarimetric synthetic aperture radar tomography of forested areas using wavelet-based distribution compressive sensing

Liang

Gao

et al. 2015

J. Appl. Remote Sens

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Abstract. The three-dimensional (3-D) structure of forests, especially the vertical structure, is an important parameter of forest ecosystem modeling for monitoring ecological change. Synthetic aperture radar tomography (TomoSAR) provides scene reflectivity estimation of vegetation along elevation coordinates. Due to the advantages of super-resolution imaging and a small number of measurements, distribution compressive sensing (DCS) inversion techniques for polarimetric SAR tomography were successfully developed and applied. This paper addresses the 3-D imaging of forested areas based on the framework of DCS using fully polarimetric (FP) multibaseline SAR interferometric (MB-InSAR) tomography at the P-band. A new DCS-based FP TomoSAR method is proposed: a new wavelet-based distributed compressive sensing FP TomoSAR method (FP-WDCS TomoSAR method). The method takes advantage of the joint sparsity between polarimetric channel signals in the wavelet domain to jointly inverse the reflectivity profiles in each channel. The method not only allows high accuracy and super-resolution imaging with a low number of acquisitions, but can also obtain the polarization information of the vertical structure of forested areas. The effectiveness of the techniques for polarimetric SAR tomography is demonstrated using FP P-band airborne datasets acquired by the ONERA SETHI airborne system over a test site in Paracou, French Guiana. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…Recent published studies demonstrate the great potential of SAR tomography in urban infrastructure monitoring [14][15][16][17][18], vehicle detection and reconstruction [19,20], and forest structure inversion [21,22], etc.. Generally, SAR tomography can be formulated as a 3D reconstruction problem in the wave number domain, or factorized as a 2D conventional SAR processing scheme plus a following 1D parameter estimation stage. The latter strategy is more preferred by researchers due to its superior efficiency, and we will concentrate on it in this study.…”

Section: Introductionmentioning

confidence: 99%

“…In [19], it is reported that the reconstruction of a hidden truck beneath foliage is greatly improved by utilizing multi-polarimetric data. Conventionally, data for different polarimetric channels are processed separately, and then fused together for more information.…”

Section: Introductionmentioning

confidence: 99%

POLARIMETRIC SAR TOMOGRAPHY USING <I>l</I><sub>2,1</sub> MIXED NORM SPARSE RECONSTRUCTION METHOD

Xing¹,

Dai²,

Li³

et al. 2012

PIER

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Abstract-The growing interest of Radar community in retrieving the 3D reflectivity map makes both polarimetric SAR interferometry and SAR tomography hot topics in recent years. It is expected that combining these two techniques would provide much better discriminating ability for scatterers lying in the same pixel. Generally, this is about reconstruction of scattering profiles from limited and irregular polarimetric measurements. As an emerging technique, Compressive Sensing (CS) provides a powerful tool to achieve the purpose. In this paper, we propose a 2,1 mixed norm sparse reconstruction method for jointly processing multibaseline PolInSAR data based on multiple measurement vector compressive sensing (MMV-CS) model, and also address the signal leakage problem with MMV-CS inversion by presenting a window based iterative algorithm. The results obtained by processing simulated data show that the proposed method possesses superior performance advantage over existing methods.

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First 3-D Reconstructions of Targets Hidden Beneath Foliage by Means of Polarimetric SAR Tomography

Abstract: This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.

Cited by 59 publications

References 16 publications

Monitoring of Forest Structure Dynamics by Means of L-Band SAR Tomography

Monitoring of Forest Structure Dynamics by Means of L-Band SAR Tomography

Multibaseline polarimetric synthetic aperture radar tomography of forested areas using wavelet-based distribution compressive sensing

POLARIMETRIC SAR TOMOGRAPHY USING <I>l</I><sub>2,1</sub> MIXED NORM SPARSE RECONSTRUCTION METHOD

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