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One of the main objectives of the upcoming space missions, such as Tandem-L and BIOMASS, is to map, on a global scale, the forest structure by means of synthetic aperture radar (SAR) tomography (TomoSAR). On one hand, the number of baselines is constrained to the revisit time that avoids temporal decorrelation issues. On the other hand, enhanced resolution is desired, since the forest structure is characterized from the vegetation layers that compose it, reflected in the tomographic profiles as local maxima. The TomoSAR nonlinear ill-conditioned inverse problem is conventionally tackled within the direction-of-arrival (DOA) estimation framework. The DOA-inspired nonparametric techniques are well suited to cope with distributed targets; nonetheless, the achievable resolution highly depends on the span of the tomographic aperture. Alternatively, superresolved parametric approaches have the main drawback related to the white noise model assumption that guaranties the separation of the signal and noise subspaces. Overcoming the disadvantages of the aforementioned techniques, in this letter, we address a novel maximum-likelihood (ML) inspired adaptive robust iterative approach (MARIA) for feature-enhanced TomoSAR reconstruction. MARIA performs resolution enhancement, with suppression of artifacts and ambiguity levels reduction, to an initial estimate of the continuous power spectrum pattern. After convergence, an accurate location of the closely spaced phase centers is achieved, easing the characterization of the forest structure. The feature-enhancing capabilities of the proposed approach are corroborated using airborne F-SAR data of the German Aerospace Center (DLR). [ABSTRACT FROM AUTHOR]

Despite its ability to characterize 3-D environments, synthetic aperture radar (SAR) tomographic imaging, when applied to the characterization of targets concealed beneath forest canopies, may appear as an ill-conditioned estimation problem, with a complex mixture of numerous scattering mechanisms measured from a few different positions. Among the set of tomographic estimators that may be used to characterize such complex scattering environments, nonparametric tomographic techniques are more robust to focus on artifacts but limited in resolution and, hence, may fail to discriminate objects, whereas parametric ones provide better vertical resolution but cannot adequately handle continuously distributed volumetric scattering densities, characteristic of forest canopies. This letter addresses a new wavelet-based sparse tomographic estimation method for the 3-D imaging and discrimination of underfoliage objects that overcomes these limitations. The effectiveness of this new approach is demonstrated using L-band airborne tomographic SAR data acquired by the German Aerospace Center over Dornstetten, Germany. [ABSTRACT FROM PUBLISHER]

In the last few years, interest in multicircular synthetic aperture radar (SAR) acquisitions has arisen as a consequence of the potential achievement of full 3-D reconstructions at very high resolution over 360° azimuth angle variation. In particular, SAR systems at low frequencies are sensitive to volumetric backscattering of semi-transparent media, and they allow the imaging of internal structures, such as forests. To achieve a full 3-D reconstruction, a 2-D synthetic aperture is required, consisting of a circular (azimuthal) and a vertical component. This 3-D capability can be understood as the result of the combination of holographic and tomographic techniques. In this paper, both techniques will be presented to establish the concept of holographic SAR tomography (HoloSAR). As a further investigation, this paper also presents an analytical expression of the 3-D impulse response function (IRF) of targets in and off the center of the illuminated area. The IRF is characterized by its spatial resolution and sidelobe power, both being a function of the radar resolution capabilities and the geometric acquisition. The second part of this paper presents a polarimetric analysis of HoloSAR tomograms. In particular, the polarimetric signature of scatterers in forested areas is investigated for three different focusing approaches, namely coherent imaging, incoherent imaging, and the generalized likelihood ratio test (GLRT). The three algorithms use the fast-factorized back-projection (FFBP) for individual circular trajectories, and the latter two use in addition compressive sensing (CS) to retrieve the complex reflectivity in elevation. The IRF is validated using a polarimetric L-band HoloSAR survey, which consists of 19 circular passes conducted by the German Aerospace Center's airborne F-SAR sensor over a test site in Kaufbeuren, Germany. The same data set is used for the analysis of the backscattering of forests. Results show a significant horizontal resolution improvement for distributed targets with the coherent imaging approach, whereas a better sidelobe suppression in the direction perpendicular to the line of sight is achieved for the incoherent imaging and the GLRT. [ABSTRACT FROM AUTHOR]

This paper presents the first fully polarimetric high-resolution circular synthetic aperture radar (CSAR) images at L-band (1.3 GHz). The circular data were acquired in 2008 by the Experimental SAR (E-SAR) airborne system of the German Aerospace Center (DLR) over the airport of Kaufbeuren, Germany. The obtained images resulting from the coherent integration of the whole circular flight are investigated and discussed in terms of two of the main CSAR properties, namely, the theoretical subwavelength resolution in the horizontal plane (x, y) and the 3-D imaging capabilities. The 3-D imaging capabilities are of special interest due to the penetration of L-band in vegetated areas. These results were compared with images processed by the incoherent addition of the full synthetic aperture. The coherent approach showed a better performance since scatterers are focused at their maximum resolution. Due to the nonlinearity of the tracks and the high-computational burden, an efficient fast factorized back-projection (FFBP) has been developed. Unlike frequencydomain processors, it accommodates azimuthal variances and topography changes. Limits and considerations of the proposed algorithm are described and discussed. To further accelerate this process, the FFBP was also implemented in a graphics processing unit (GPU). Processing performance has been assessed with the direct BP (DBP) as a reference, obtaining speedup factors up to 1800. Residual motion errors have been estimated with a new frequency-based autofocus approach for CSAR configurations based on low signal-to-clutter ratio (SCR) isotropic scatterers. High-resolution images of man-made and distributed scatterers have been analyzed and compared with a stripmap SAR, both concerning anisotropic and isotropic-like scatterers. Results include a single-channel tomogram of a Luneburg lens and a fully polarimetric tomogram of a tree. [ABSTRACT FROM PUBLISHER]

Multicircular synthetic aperture radar (SAR) (MCSAR) is an extension of circular SAR (CSAR) characterized by the formation of a synthetic aperture in elevation with several circular flights. This imaging mode allows an improved resolution in the plane perpendicular to the line of sight ( \LOS\perp), thus suppressing the 3-D cone-shaped sidelobes that are formed when focusing with CSAR. This letter presents the first polarimetric MCSAR airborne experiment acquired at P-band by the German Aerospace Center (DLR)'s F-SAR system over a forested area in Vordemwald, Switzerland. This letter also includes a phase calibration method based on the singular value decomposition (SVD) using ground signatures to estimate constant phase offsets within a stack of 2-D images. Focusing methods, such as fast-factorized back projection (FFBP), beamforming (BF), and compressive sensing (CS), described in previous publications are used to solve the complex reflectivity in the (x, y, z) space. [ABSTRACT FROM PUBLISHER]

Synthetic aperture radar (SAR) tomography is a 3-D imaging modality that is commonly tackled by spectral estimation techniques. Thus, the backscattered power along the cross-range direction can be readily obtained by computing the Fourier spectrum of a stack of multibaseline measurements. In addition, recent work has addressed the tomographic inversion under the framework of compressed sensing, thereby recovering sparse cross-range profiles from a reduced set of measurements. This paper differs from previous publications, in that it focuses on sparse expansions in the wavelet domain while working with the second-order statistics of the corresponding multibaseline measurements. In this regard, we elaborate on the conditions under which this perspective is applicable to forested areas and discuss the possibility of optimizing the acquisition geometry. Finally, we compare this approach with traditional nonparametric ones and validate it by using fully polarimetric L-band data acquired by the Experimental SAR (E-SAR) sensor of the German Aerospace Center (DLR). [ABSTRACT FROM AUTHOR]

Super-resolution imaging via compressed sensing (CS)-based spectral estimators has been recently introduced to synthetic aperture radar (SAR) tomography. In the case of partial scatterers, the mainstream has so far been twofold, in that the tomographic reconstruction is conducted by either directly working with multiple looks and/or polarimetric channels or by exploiting the corresponding single-channel second-order statistics. In this letter, we unify these two methodologies in the context of covariance fitting. In essence, we exploit the fact that both vertical structures and the unknown polarimetric signatures can be approximated in a low-dimensional subspace. For this purpose, we make use of a wavelet basis in order to sparsely represent vertical structures. Additionally, we synthesize a data-adaptive orthonormal basis that spans the space of polarimetric signatures. Finally, we validate this approach by using fully polarimetric L-band data acquired by the E-SAR sensor of the German Aerospace Center (DLR). [ABSTRACT FROM PUBLISHER]

In recent years, 3-D imaging by means of polarimetric synthetic aperture radar (SAR) sensors has become a field of intensive research. In SAR tomography, the vertical reflectivity function for every azimuth–range pixel is usually recovered by processing data collected using a defined repeat-pass acquisition geometry. The most common approach is to generate a synthetic aperture in the elevation direction through imaging from a large number of parallel tracks. This imaging technique is appealing, since it is very simple. However, it has the drawback that large temporal baselines can severely affect the reconstruction. In an attempt to reduce the number of parallel tracks, we propose a new approach that exploits structural correlations between neighboring azimuth–range pixels and/or polarimetric channels. As a matter of fact, this can be done under the framework of distributed compressed sensing (CS) (DCS), which stems from CS theory, thus also exploiting sparsity in the tomographic signal. Finally, results demonstrating the potential of the DCS methodology will be validated by using fully polarimetric L-band data acquired by the E-SAR sensor of the German Aerospace Center (DLR). [ABSTRACT FROM PUBLISHER]

This paper addresses the imaging of objects located under a forest cover using polarimetric synthetic aperture radar tomography (POLTOMSAR) at L-band. High-resolution spectral estimators, able to accurately discriminate multiple scattering centers in the vertical direction, are used to separate the response of objects and vehicles embedded in a volumetric background. A new polarimetric spectral analysis technique is introduced and is shown to improve the estimation accuracy of the vertical position of both artificial scatterers and natural environments. This approach provides optimal polarimetric features that may be used to further characterize the objects under analysis. The effectiveness of this novel technique for POLTOMSAR is demonstrated using fully polarimetric L-band airborne data sets acquired by the German Aerospace Center (DLR)'s E-SAR system over the test site in Dornstetten, Germany. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) tomography (TomoSAR) is a multibaseline interferometric technique that estimates the power spectrum pattern (PSP) along the perpendicular to the line-of-sight (PLOS) direction. TomoSAR achieves the separation of individual scatterers in layover areas, allowing for the 3D representation of urban zones. These scenes are typically characterized by buildings of different heights, with layover between the facades of the higher structures, the rooftop of the smaller edifices and the ground surface. Multilooking, as required by most spectral estimation techniques, reduces the azimuth-range spatial resolution, since it is accomplished through the averaging of adjacent values, e.g., via Boxcar filtering. Consequently, with the aim of avoiding the spatial mixture of sources due to multilooking, this article proposes a novel methodology to perform single-look TomoSAR over urban areas. First, a robust version of Capon is applied to focus the TomoSAR data, being robust against the rank-deficiencies of the data covariance matrices. Afterward, the recovered PSP is refined using statistical regularization, attaining resolution enhancement, suppression of artifacts and reduction of the ambiguity levels. The capabilities of the proposed methodology are demonstrated by means of strip-map airborne data of the Jet Propulsion Laboratory (JPL) and the National Aeronautics and Space Administration (NASA), acquired by the uninhabited aerial vehicle SAR (UAVSAR) system over the urban area of Munich, Germany in 2015. Making use of multipolarization data [horizontal/horizontal (HH), horizontal/vertical (HV) and vertical/vertical (VV)], a comparative analysis against popular focusing techniques for urban monitoring (i.e., matched filtering, Capon and compressive sensing (CS)) is addressed. [ABSTRACT FROM AUTHOR]

Future SAR missions will provide three-dimensional images of semi-transparent media, such as vegetation and ice, through SAR tomography. Access to information on the internal structure of these volume scatterers is a key factor for a better understanding of ecosystem dynamics and climate change. Because of this, several concepts are nowadays examined to implement SAR tomography in a spaceborne framework. In order to do that, it is necessary to gather different observations of the area of interest. Unfortunately, a consequence of the time that elapses between acquisitions is that the electromagnetic properties of the medium may vary. This implies that, there may be inconsistencies in the acquired data, leading to errors in the final inversion. A solution to partially cope with this temporal decorrelation, is to acquire data employing two or more sensors operating with a reduced (or even absent) temporal gap and then to collect several acquisitions at different time instants. By means of this imaging concept, the required line-of-sight diversity is granted and the desired resolution in the height direction ensured. In this way, sets of temporal decorrelation-free interferometric coherences can be built and the vertical scattering profile can be retrieved via coherence-based tomography. This contribution analyzes a two-sensor system like TanDEM-X (Krieger et al., 2007), Tandem-L (Moreira et al., 2015), or SAOCOM-CS (Davidson et al., 2014). In particular, the potential of coherence-based tomography are shown with data acquired with the TanDEM-X sensors for boreal and Amazon forest. In addition, a technique to partially cope with temporal decorrelation through covariance matrix filtering is also presented in the paper. • 3-D SAR tomographic reconstruction of vegetation with spaceborne TanDEM-X data • Retrievals for boreal and tropical forest • Coherence-based processing philosophy to tackle temporal decorrelation • A covariance matrix filtering approach can further mitigate temporal changes. [ABSTRACT FROM AUTHOR]