Synthetic aperture radar tomography (TomoSAR) uses several coregistered images from different perspectives to reconstruct a power spectrum pattern (PSP) perpendicular to the line of sight (PLOS), enabling the estimation of a 3-D representation of the area. Classical estimators exhibit ambiguities and other undesired effects that are stronger for sparser and smaller stacks. To mitigate the limitations arising from a restricted number of acquisitions, we propose using a deep neural network (NN) to synthesize artificial tracks (i.e., images not contained in the original stack). The presented method utilizes a convolutional NN with an encoder-decoder architecture. We evaluate the proposed approach on real TomoSAR data from an airborne campaign over a forest region. The view estimation improves the tomographic results, offering robustness to scenarios affected by temporal decorrelation, which other classical methods, such as cubic convolution (CC), do not provide.

Most common focusing techniques for Synthetic Aperture Radar (SAR) Tomography (TomoSAR), e.g. Matched Spatial Filtering and Capon, make use of the conventional sample covariance matrix, obtained from a finite number of observations. Yet, structured covariance matrix estimates can be employed in lieu of the sample covariance matrix. Accordingly, our simulation study shows that Capon’s performance improves with the use of structured covariance matrices. These are obtained with the Subspace Fitting approach, properly adapted to TomoSAR. Numerical comparisons between structure and unstructured covariance matrices are presented.

Nonlinear inverse problems like Synthetic Aperture Radar Tomography are often ill-posed, since their solutions are very sensitive to small perturbations in the input data and are, therefore, difficult to compute numerically. Ill-posed problems are commonly tackled with regularization approaches; however, there is a crucial problem in regularization, related to the selection of regularization parameters. In the search for optimal values of such regularization parameters, this article addresses an extension of the L-curve method, called Θ-curve. Furthermore, aimed at reducing converge time, the k-criterion is added, based on the first and second derivatives of the L-curve.

This paper provides an overview of the state of the art and an outlook on future developments of spaceborne Synthetic Aperture Radar (SAR) systems with multi-baseline imaging capability, such as 3D differential SAR interferometry (3D-DinSAR), polarimetric SAR interferometry (Pol-InSAR), tomography (TomoSAR), and holography (HoloSAR). The goal is to fill the multidimensional data space with additional information from images with different spatial and/or temporal baselines.

This paper presents our new SAR processing framework known as PRISM (Processor for Interferometric SAR Missions). This flexible approach allows the efficient and accurate processing of SAR data independent of the sensor and the acquisition mode. The two main PRISM components (the focusing and the interferometric chains) are described in this paper together with the philosophy of the software architecture. Experimental results are presented and discussed based on the impulse response function analysis of simulated data as well as the focusing and interferometric results using real TerraSAR-X data.

Polarimetric focusing techniques for synthetic aperture radar (SAR) tomography (TomoSAR) pursue finding optimal polarization combinations to extract the associated scattering mechanisms and height of reflectors. Regularization approaches like weighted covariance fitting (WCF), implemented in an iterative manner, attain finer resolution than conventional focusing techniques (e.g., Capon). Such approaches normally entail the selection of a regularization parameter and a first estimate of the power spectrum pattern. Regularization parameter selection via L-Curve method requires providing the scattering vector; nonetheless, it may not be always available, especially when not working at full resolution. Manipulations previously done to the data covariance matrix (e.g., pre-summing) must be equivalent in the scattering vector, which may not be at all times feasible. Accordingly, this article suggests modifying the L-Curve method to work exclusively with data covariance matrices. The proposed novel strategy is applied to WCF, considering single and dual channels. Iterations are stopped based on the Akaike information criterion.

The correct functioning of parametric focusing techniques [e.g., MUltiple SIgnal Classification (MUSIC)] require a proper selection of the model order. For such aim, a methodology based on the Kullback-Leibler information criterion is commonly employed. These methods perform well due to its propensity to choose relatively large model orders, which tend to retrieve good-fitted responses when the data generating mechanism is more complex than the models used to fit. However, some solutions can be misleading, since only the most proper model order (i.e., the actual number of targets) guaranties best performance. As an alternative, this work suggests employing statistical regularization instead of model order selection (MOS) approaches. First, a model with large order is chosen to perform focusing via parametric methods; subsequently, statistical regularization is applied, seeking to attain good-fitted solutions. To demonstrate the capabilities of the addressed novel strategy, Synthetic Aperture Radar (SAR) Tomography (TomoSAR) is considered as application.

The BIOMASS DEM Product Prototype Processor (BIO-DEMPP) is being developed in the frame of ESA's Earth Explorer BIOMASS mission. The prototype includes a complete interferometric SAR chain, from the stack co-registration until the mosaicking of the derived height products (Digital Elevation and Digital Terrain Models). This paper presents an overview of the BIODEMPP architectural design and its validation strategy, as well as first results obtained with simulated BIOMASS-like data.

Synthetic aperture radar (SAR) tomography (TomoSAR) is a remote sensing technique that allows for the 3-D representation of the illuminated areas, recovering the vertical distribution of the backscattered power at each range-azimuth position. In this context, and with the aim of retrieving feature-enhanced tomograms, this paper addresses a new multi-stage iterative method that operates robustly in real-world TomoSAR operating scenarios with irregularly distributed acquisition constellations and only few available looks. The addressed approach alleviates the drawbacks of the conventional matched spatial filter (MSF) technique, which retrieves high ambiguity levels when irregular sampling is considered. Also, it alleviates the drawbacks of the commonly used Capon beamforming technique, which results to be inapplicable when the involved data covariance (structure) matrices are rank deficient. The addressed novel method combines the descriptive experiment design regularization (DEDR) framework for enhanced image reconstruction, with the wavelet domain thresholding (WDT)-based sparsity promoting refinement in the wavelet transform (WT) domain. The capabilities of the addressed WDT-refined virtual adaptive beamforming (VAB) approach, which we refer to as WAVAB, are corroborated via processing P-band airborne TomoSAR data of the German Aerospace Center (DLR), acquired by the E-SAR system over the test site located at the Vindeln municipality, northern Sweden, in 2008.

In May-June 2015 UAVSAR was flown to Europe to collect data in support of experiments in Iceland, Norway and Ger-many. The deployment in Germany was focused on PolIn-SAR and tomographic data collections at the Traunstein Forest and in the Munich urban area. In this paper we describe tomographic processing of the Munich data and comparison with in situ ground truth data.

Speckle is inherent to all coherent imaging systems and affects SAR imagery in the form of strong intensity variations in pixels with similar backscattering coefficient, difficulting the interpretation of SAR data. In the context of the Beltrami filter, a polarimetric distance is utilized as part of a region growing algorithm to find and then average similar covariance matrices within a central window using an iterative scheme. The Beltrami filter has shown good results using a computationally expensive geodesic distance that takes into account the Hermitian positive definite nature of the polarimetric covariance matrices. The flexible nature of the Beltrami distance allows for the use of any polarimetric distance, allowing the study on the utilization of less computationally complex distances and their impact on speckle reduction. In this paper, an analysis on the effect of some of the commonly utilized polarimetric synthetic aperture radar (PolSAR) distances measures within the Beltrami despeckling filter will be presented.

Synthetic aperture radar (SAR) tomography (TomoSAR) employs array signal processing techniques, in order to estimate the location of the vertical structures that compose the backscattering field, in the direction perpendicular to the line-of-sight (PLOS). Due to the limited number of tracks for practical TomoSAR sensing scenarios, it becomes challenging to accurately estimate the source parameters. Additionally, irregular sampling and non-uniform acquisition constellations introduce artifacts and increase ambiguity. The usage of super-resolved parametric methods and compressed sensing (CS) based approaches, improve the vertical resolution and mitigate the effect of sidelobes. However, parametric approaches have the main drawback related to the assumption that the scene is composed by a known finite number of point-type backscattering sources. Also, the CS-based techniques regularly imply a considerable computational burden. Overcoming the disadvantages of the above mentioned TomoSAR-adapted methods, this paper presents a novel non-parametric iterative approach for feature enhanced SAR tomography, in the context of maximum likelihood (ML) estimation theory. The feature enhancing capabilities of the proposed technique are corroborated via processing L-band airborne TomoSAR data of the German Aerospace Center (DLR), acquired by the F-SAR system over the forested test site of Froschham, Germany, in 2017.

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).

The ground-volume separation of radar scattering plays an important role in the analysis of forested scenes. For this purpose, the data covariance matrix of multi-polarimetric (MP) multi-baseline (MB) SAR surveys can be represented thru a sum of two Kronecker products composed of the data covariance matrices and polarimetric signatures that correspond to the ground and canopy scattering mechanisms (SMs), respectively. The sum of Kronecker products (SKP) decomposition allows the use of different tomographic SAR focusing methods on the ground and canopy structural components separately, nevertheless, the main drawback of this technique relates to the rank-deficiencies of the resultant data covariance matrices, which restrict the usage of the adaptive beamforming techniques, requiring more advanced beamforming methods, such as compressed sensing (CS). This paper proposes a modification of the nonparametric iterative adaptive approach for amplitude and phase estimation (IAA-APES), which applied to MP-MB SAR data, serves as an alternative to the SKP-based techniques for ground-volume reconstruction, which main advantage relates precisely to the non-need of the SKP decomposition technique as a pre-processing step.

Modern approaches for resolution enhancement (RE) and superresolution (SR) of coherent remote sensing (RS) imagery suggest to exploit the sparsity of the desired image representations in some appropriately chosen overcomplete dictionaries and treat the related RE/SR imaging inverse problems in descriptive settings imposing some structured regularization constraints. However, such approaches are not properly adapted to the SR recovery of the speckle-corrupted low resolution (LR) coherent radar imagery with preservation of salient image features. In this letter, we address a new multistage iterative SR technique for feature-enhanced radar/fractional synthetic aperture radar computational imaging. First, the despeckled high-resolution image is recovered from the LR speckle-corrupted radar image applying the descriptive-experiment-design-regularization-based reconstructive processing. Next, the multistage RE is consequently performed in each nested refined SR frame via the iterative reconstruction of the upscaled radar images, followed by the discrete-wavelet-transform-based sparsity-promoting denoising with guaranteed consistency preservation in each resolution frame.

The aim of this study is to address a new approach and develop the relevant technique for super-resolution (SR) feature-enhanced recovery of microwave remote sensing (RS) imagery. The challenging proposition is twofold. First, we adapt the SR multi-scale iterative reconstructive (MSIR) image post-processing method for solving the inverse problem of recovery of the speckle corrupted low resolution RS images employing iterative projections onto the nested refined resolution frames. Second, we unify the modified RS-adapted MSIR method with the Descriptive Experiment Design Regularization (DEDR) high-resolution RS image enhancement technique for attaining the overall SR recovery with considerably enhanced resolution performances. Algorithmically, the MSIR processing loop is performed via the Fourier transform (FT) or wavelet transform (WT) of the input image. Different wavelet dictionaries were examined in order to approach the most speeded-up WT-based iterative MSIR-level image recovery.

We address a new approach to a reconstructive imaging inverse problems solution as required for enhancement of low resolution real aperture radar/fractional SAR imagery in harsh sensing environments. To preserve the image and image gradient map sparsity peculiar for real-world remote sensing (RS) scenarios, we aggregate the minimum risk inspired descriptive experiment design regularization (DEDR) framework for balanced image resolution enhancement over noise suppression with two additional regularization levels: (i) the variational analysis inspired minimization of the image total variation (TV) map and (ii) the sparsity preserving regularizing projections onto convex solution sets (POCS). The new framework incorporates the TV metric structured regularization into the weighted l2metric structured DEDR data agreement objective function and suggests the solver for the overall reconstructive imaging inverse problem employing the DEDR-TV-POCS-restructured MVDR strategy. The DEDR-TV-POCS method implemented in an implicit iterative fashion outperforms the competing nonparametric adaptive radar imaging techniques both in the resolution enhancement and computational complexity reduction as verified in the reported simulations.

We address a new approach for enhanced microwave remote sensing (RS) imaging via performing the imaging system kernel point spread function (PSF) operator refinement-based multi-scale iterative reconstructive (MSIR) image post-processing, as required for emerging feature enhanced RS missions. The high-resolution (HR) image is first reconstructed from the initial low-resolution (LR) image employing the statistically optimal minimum risk inspired descriptive experiment design regularization (DEDR) framework. Next, to approach the overall super-resolution (SR) imaging performances we incorporate into the DEDR method the additional postprocessing stage aimed at filling in the null space of the HR imaging system PSF operator via performing the corresponding projections onto the nested refined resolution greed frames. We feature the differences between the proposed DEDR-MSIR resolution refinement approach and the most competing celebrated Papoullis-Gerchberg SR method adapted for the feature enhanced RS imaging and demonstrate the advantages of the unified DEDR-MSIR approach for SR image recovery.