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.

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.

Synthetic aperture radar (SAR) polarimetry can improve change detection in terms of detection capabilities. In this work, we are proposing to extend the idea of target decomposition to changes affecting partial targets. This will allow the separation of polarimetric-dependent changes, providing extra information that can be used to better understand the processes affecting the targets. Three models for changes are proposed and compared. The methodologies are based on Lagrangian optimizations of distinct operators built using quadratic forms for a power ratio and a power difference. The optimizations can be accomplished by diagonalizations of specific matrices derived from polarimetric covariance matrices. These are, therefore, spectral decompositions of an appropriate matrix which we define as change matrix. The theoretical validity of the models is assessed using Monte Carlo simulations. Additionally, we perform real data validation exploiting L-band quad-polarimetric data from the E-SAR (DLR) SARTOM 2006 campaign and ALOS PALSAR (JAXA) acquisitions in Morecombe Bay (U.K.). We observed that the two algorithms based on power difference allow to decompose the change into the minimal set of scattering mechanisms (SMs) that have been added or removed from the scene. The two algorithms differ on the initial assumption on the change. On the other hand, the ratio operator provides a better detection performance although the eigenvalues do not correspond to meaningful SMs. A combination of the three methodologies can, therefore, improve detection and classification of changes.

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.

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.

In this paper data acquired at different frequencies, namely X- and L-band are exploited to perform DInSAR analysis. The objective of this work is to present strategies to apply DInSAR by evaluating the required processing aspects to jointly estimate the deformation for multi-frequency time series. The X-band data are acquired with the TerraSAR-X sensor, while the L-band ones are ALOS/ALOS-2 data. The present work is the result of the ongoing “inter agency arrangement for strategic partnership” between the German Aerospace Center (DLR) and the Japanese Aerospace Exploration Agency (JAXA) concerning Multi-temporal InSAR analysis.

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).

This contribution is dedicated to present tomographic investigations on 3D vegetation imaging for future spaceborne SAR missions. The main problem to tackle when performing tomography via repeat-pass spaceborne data is that the temporal decorrelation between acquisitions can be very severe making it difficult to achieve reliable results. In this context, if two or more sensors are available to perform the surveys, a set of quasi-simultaneous data can be achieved for a certain time instant. It is understood that for such data the temporal decorrelation effect as well as the atmospheric artefacts will be strongly mitigated. By varying the acquisition geometry, it is in principle now possible to achieve cross-range resolution and retrieve the vertical profile via SAR tomography. The present paper focuses on a two-satellite scenario like TanDEM-X [1], Tandem-L [2], SAOCOM-CS [3]. In particular, TanDEM-X data, acquired in a pursuit monostatic mode, is employed to perform the demonstration over boreal as well as tropical forest.

The contribution focuses on the current status of the ESA study entitled “InSARAP Sentinel-1 Constellation Study”, which investigates the interferometric performance of the S1A/S1B units. General aspects like the interferometric compatibility in terms of common range and Doppler bandwidth and the burst synchronization are addressed. Besides the first interferometric results with both units, time series results over the pilot sites combining both satellites are also shown, as well as some investigations with fast moving (i.e., glaciers) scenarios.

The contribution focuses on the technical aspects related to the focusing and interferometric processing of bistatic data acquired by companion satellite (CS) SAR missions. In particular, the processing aspects related to the large along-track baseline configuration will be addressed, for the processing needs to properly consider a potential high squint angle. The technical challenges encompass synchronization, image formation and coregistration. On the other hand, the availability of a large squint angle has some advantages in terms of the sensitivity to the north-south component of the deformation for interferometric applications. The theoretical derivations are complemented with point-target simulations and Monte-Carlo simulations.

Companion SAR missions offer a cost-effective solution to enhance the added value of existing SAR satellites. In particular, they offer new possibilities to extend the observation space and provide one or several single-pass interferometric channels for the acquisitions. The paper aims to provide a description of an end-to-end performance simulation for companion SAR missions, including the evaluation of two exemplary configurations under realistic conditions and with the incorporation of the major error sources affecting these systems. A critical discussion of the results will be included of the final version of the paper drawing from the analysis relevant conclusions for the design of payload and concepts for future companion SAR missions.

This paper provides an overview of the future development of spaceborne SAR systems with multi-baseline imaging capability like polarimetric SAR interferometry (PolInSAR), tomography (TomoSAR) and holography (HoloSAR). The goal is to fill the multi-dimensional data space with additional information from acquisitions having different spatial or temporal baselines. Multi-baseline imaging opens the door for a new class of image products in spaceborne SAR. Well-known examples are across-track and along-track interferometry, which allow the measurement of surface topography, ground deformation, ocean currents as well as glacier movements. While across-track and along-track interferometry are well established techniques and have been widely used by current spaceborne SAR systems, PolInSAR, TomoSAR and HoloSAR are emerging techniques which are shaping the future development of spaceborne SAR. New mission concepts for multi-static SAR configurations with distributed and sparse arrays will pave the way for this development.

This paper presents results of the Sentinel-1 sensor in the interferometric wide-swath (IW) mode encompassing the first two years of operation of the mission. The paper focuses on persistent scatterer interferometric results and their validation. Further applications and investigations are also addressed, e.g., earthquakes, volcanoes and tomography.

Companion SAR missions offer a cost-effective solution to boost the performance and capabilities of existing SAR missions. With the use of companion satellites, a whole suite of coherent single-pass observations become feasible, allowing for the interferometric and tomographic exploitation of the received data. Interferometric and tomographic measurements are however very sensitive to even the slightest degradations in the phase of the received data, and impose stringent requirements on the calibration concept of the mission. Weakly-synchronised companion SAR missions may have significant residual clock errors, which challenge the attainment of this requirements. We put forward an innovative calibration concept based on autonomous (data-based) calibration at different product levels and present an estimation of the expected performance for ESA's SAOCOM/CS mission.

In this paper we investigate the surface displacement pattern before and after the 2011 Tohoku earthquake using persistent scatterer interferometry of ALOS/PALSAR and TerraSAR-X images. For confirming the accuracy of the estimated displacement, interferometric phase components were evaluated and simulated data were created. Using the simulated data, the accuracy of filtering methods to mitigate tropospheric methods was evaluated. Real data processing clarified detailed spatial patterns of previously unrecognized displacement. Comparison with GPS data showed that the estimated displacement was within the expected accuracy of the simulated data. Our results demonstrate the effectiveness of the combination of ALOS/PALSAR and TerraSAR-X data analysis, and the accuracy evaluation by quantifying interferometric phase components.

This contribution reports on the performance investigations of the interferometric wide swath (IW) mode of Sentinel-1, which is implemented using the terrain observation by progressive scans (TOPS) mode. The key aspects of the TOPS mode that need to be considered for accurate interferometric processing will be presented, and first analyses with Sentinel-1 time series will be shown. The results focus on the pilot sites of Campi Flegrei/Vesuvius and Mexico City, as well as Greenland glaciers. Other aspects related to the interferometric performance are also presented, like the burst synchronization, the pointing accuracy, or the considerations when evaluating non-stationary scenes.

SAR tomography is a remote sensing technique, extending SAR interferometry, that allows three-dimensional imaging of multiple height-distributed point-like scatterers or volumetric targets. It allows, for example, to solve layover urban scatterers or to analyze the vertical structure of vegetation layers or other targets with significant penetration of the sensor's radiation (dry soil, ice layers, etc.). The ability to monitor the 3-D inner structure of volumetric targets and to extract information about the nature and location of ongoing scattering processes promises a break-through in key environmental problems. Indeed, structural parameters of volume scatterers in the biosphere and cryosphere, such as vegetation height and moisture content, forest vertical structure and biomass, or snow / ice depth and layering are critical inputs for ecological process modeling and enable effective monitoring of ecosystem change. The 3-D tomographic principle has been also extended to higher dimension in the more recent differential tomography methodology, producing 4-D (3-D + time) images bridging the gap between differential interferometry and SAR tomography. Due to their unique capabilities and potential, tomographic SAR techniques have gained significant attention during the last years and become established methods for analysis of all kinds of three-dimensional back-scattering scenarios. In particular, for future space-borne SAR sensors operating in longer wavelength, like BIOMASS, SAOCOM-CS or Tandem-L, it is planned to employ 3-D tomographic imaging modes on an operational basis. This paper aims to review the different state-of-the-art signal processing methods for three-dimensional SAR imaging, which have been developed during the last decade, and for four-dimensional differential tomography. The requirements, advantages and disadvantages of different techniques are assessed and compared and open issues to be addressed in future developments are pointed out.

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).

During the last decade, synthetic aperture radar (SAR) became an indispensable source of information in Earth observation. This has been possible mainly due to the current trend toward higher spatial resolution and novel imaging modes. A major driver for this development has been and still is the airborne SAR technology, which is usually ahead of the capabilities of spaceborne sensors by several years. Today's airborne sensors are capable of delivering high-quality SAR data with decimeter resolution and allow the development of novel approaches in data analysis and information extraction from SAR. In this paper, a review about the abilities and needs of today's very high-resolution airborne SAR sensors is given, based on and summarizing the longtime experience of the German Aerospace Center (DLR) with airborne SAR technology and its applications. A description of the specific requirements of high-resolution airborne data processing is presented, followed by an extensive overview of emerging applications of high-resolution SAR. In many cases, information extraction from high-resolution airborne SAR imagery has achieved a mature level, turning SAR technology more and more into an operational tool. Such abilities, which are today mostly limited to airborne SAR, might become typical in the next generation of spaceborne SAR missions.

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).

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 working directly with multiple looks and/or polarimetric channels or by exploiting the corresponding single-channel second order statistics. In this paper, we unify these two methodologies in the context of covariance fitting. In essence, we exploit the fact that both vertical structures as well as 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).

The use of polarimetric and interferometric SAR (Pol-InSAR) has been demonstrated to bring significant advantages for several remote sensing applications. Sometimes, the hypothesis that the scattering mechanisms (SM) do not suffer large changes in the two acquisitions is performed (ESM). On the other hand, a different set of algorithms should be employed. In this paper a new methodology is proposed to identify errors consequence of possible changes. The algorithm performs a Lagrange optimization of an error factor of the Pol-InSAR coherences, returning also the SM that change maximally and minimally during the two acquisitions. The algorithm was tested on E-SAR L-band data acquired during the AgriSAR 2006 campaign.

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).

SAR tomography (SARTom) is an imaging technique that allows multiple phase center separation in the vertical direction, leading to a 3-D reconstruction of the imaged scene. The retrieval of volume structure information (e.g., for forest classification) and the solution of the layover problem are two of the most promising applications. In this letter, SARTom, in combination with polarimetry (PolSARTom), is exploited to image and to extract characteristic features (e.g., shape and height) of targets hidden beneath foliage. This analysis is applied to L-band airborne data acquired by the E-SAR system of the German Aerospace Center (DLR) during a tomographic campaign that took place in September 2006 on the test site of Dornstetten (Germany).

TanDEM-X (TerraSAR-X Add-on for Digital Elevation Measurements) is a high-resolution interferometric mission with the main goal of providing a global and unprecedentedly accurate digital elevation model of the Earth surface by means of single-pass X-band synthetic aperture radar (SAR) interferometry. Despite its usual quasi-monostatic configuration, TanDEM-X is the first genuinely bistatic SAR system in space. During its monostatic commissioning phase, the system has been mainly operated in pursuit monostatic mode. However, some pioneering bistatic SAR experiments with both satellites commanded in nonnominal modes have been conducted with the main purpose of validating the performance of both space and ground segments in very demanding scenarios. In particular, this letter reports about the first bistatic acquisition and the first single-pass interferometric (mono-/bistatic) acquisition with TanDEM-X, addressing their innovative aspects and focusing on the analysis of the experimental results. Even in the absence of essential synchronization and calibration information, bistatic images and interferograms with similar quality to pursuit monostatic have been obtained.

In recent years, three-dimensional imaging by means of SAR tomography 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 draw back that large temporal baselines, which is the case for space-borne platforms, can severely affect the reconstruction. In an attempt to reduce the number of parallel tracks, we propose a new tomographic focusing approach that trades number of SAR images for correlations between neighboring azimuth-range pixels and polarimetric channels. As a matter of fact, this can be done under the framework of Distributed Compressed Sensing (DCS), which stems from Compressed Sensing (CS) theory, thus also exploiting sparsity in our tomographic signal. In addition, we address the problem of measurements affected by additive as well as multiplicative speckle noise. 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 DLR.

TanDEM-X is a high-resolution interferometric mission with the main goal of providing a global and unprecedentedly accurate digital elevation model (DEM) of the Earth surface by means of single-pass X-band SAR interferometry. De spite its usual quasi-monostatic configuration, TanDEM-X is the first genuinely bistatic SAR system in space. During its monostatic commissioning phase, the system has been mainly operated in pursuit monostatic mode. However, some pioneering bistatic SAR experiments with both satellites commanded in non-nominal modes have been conducted with the main purpose of testing the performance of both space and ground segments in very demanding scenarios. In particular, this paper reports about the first bistatic acquisition and the first single-pass interferometric (mono/bistatic) acquisition with TanDEM-X. Even in the absence of essential synchronisation and calibration information, bistatic images and interferograms with similar quality to pursuit monostatic have been obtained.

TanDEM-X is a high-resolution interferometric radar mission with the main goal of providing a global digital elevation model (DEM) of the Earth surface by means of single-pass X-band SAR interferometry. It is, moreover, the first genuinely bistatic spaceborne SAR mission, and, independently of its usual quasi-monostatic configuration, includes many of the peculiarities of bistatic SAR. An experimental, versatile, and flexible interferometric chain has been developed at DLR Microwaves and Radar Institute for the scientific exploitation of TanDEM-X data acquired in non-standard configurations. The paper describes the structure of the processing chain and focusses on some essential aspects of its bistatic part. Some experimental results performed with TerraSAR-X demonstrate the flexibility of the implemented processor.

The study of data acquired over a circular trajectory has raised an increasing interest in the SAR community. Two main reasons summarize the interest in such geometry. First, sub-wavelength resolution can be achieved, as the targets in the spotted area are observed under a 360oaperture. Second, the use of the information from different azimuthal directions allows one to obtain information of the scene in the third dimension, making possible a 3D target reconstruction. In any case, both applications require certain target reflectivity homogeneity. This paper shows several processing results and analyzes the potentials and limitations of circular SAR to perform tomography of semi-transparent media. Special processing aspects, like the estimation of residual motion errors due to inaccuracies in the navigation data, are also addressed. Data acquired at L-band by DLR's E-SAR system are used to demonstrate the high resolution and tomographic imaging capabilities of circular SAR. The results include the tomogram of a Luneburg lens, as well as preliminary results over man-made targets and vegetation.

In SAR tomography, the available information in the height direction is limited by the number of different tracks that can be acquired in practice. To counterbalance this limitation, electromagnetic scattering models, properly exploiting the available a priori information, should be used, the ¿few¿ possible acquisitions should be accurately selected, and processing algorithms, guaranteeing the reliability of the results, should be employed. We present an approach for the reconstruction of the vertical reflectivity distribution of vegetated areas facing the above three points. In particular, an eigenvalue optimization procedure is exploited to design an ¿optimal¿ constellation to be flown, the peculiar features of ground and canopy are accounted for and an effective global optimization algorithm is used to enhance the reliability. The approach is tested with both simulated and real data acquired by the E-SAR system of DLR over the Dornstetten forest test-site.

Synthetic aperture radar tomography (SARTom) is the natural extension of SAR interferometry to solve for multiple phase centers within a resolution cell and obtain the 3-D representation of a scene. This paper deals with the determination of the minimum number of tracks required to perform SARTom. Through the prolate spheroidal wave functions, the number of equivalent targets of a volumetric source is derived, and from it, the minimum number of observations required to apply subspace superresolution methods is computed. The minimum tomographic aperture length is also investigated. The results are validated on real data acquired in L-band by the experimental SAR system of the German Aerospace Center.

The main drawback of SAR Tomography (SARTom) is the considerable number of tracks required to achieve the 3-dimensional (3D) representation of a viewed scene. The key point concerns the trade-off between the vertical resolution and the control on ambiguities phenomena. This paper deals with the problem of the determination of the minimum number of required tracks when super-resolution subspace methods are applied. The results are validated on real data acquired in L-band by the E-SAR system of the German Aerospace Centre.

SAR tomography (SARTom) is an imaging technique that allows multiple phase centers separation in the vertical (height) direction. It is performed after standard 2D SAR repeat-pass processing and operates on a stack of coregistered SAR images. Theoretically, the coregistration between two images is height dependent and the use of a reference height (or a DEM) is needed, although not ideal in the case of volumetric target (multiple phase centers in one resolution cell). In this paper, the drawbacks related to the choice of this reference in a tomographic context are analysed and a height dependent coregistration approach is proposed. In order to do this, it is also necessary to remove processing corrections related to the reference height, such as motion compensation, and make them height dependent. The inclusion of the height dependency during the tomographic SAR processing results in a better quality of the final tomograms in terms of pseudo-power and phase centers separation. The results of the proposed approach are validated on real data acquired by the E-SAR system of the German Aerospace Centre - DLR.

The aim of this work is to exploit the MUSIC algorithm performance in order to enhance target separability in range and azimuth, i.e. achieve point targets separation inside a resolution cell. Simulations have been done in order to plan and check the feasibility of a super-resolution experiment that took place in September 2006 on the test site of Oberpfaffenhofen (Germany). The data set has been acquired with the E-SAR system of the DLR in X-band. The targets to be separated were seven small corner reflectors that have been placed in a way that their response falls in one or, at maximum, two resolution cells of the standard Fourier SAR image. A post-processing implementation of the MUSIC algorithm has been proposed allowing, in the already focused SAR image, to retrieve the targets geometry. Conditions and analysis of the results have been carried out.