The distribution of the space key resources, usually concentrated in a single, large, and complex satellite, can be shared among small-sized and simpler systems, thanks to the proper combination of the signals from each single node of the swarm. A proper sensor positioning, and on-ground data recombination allow the azimuth ambiguities to be cancelled out, guaranteeing gain in terms of signal to noise ratio. However, sensors synchronization, position control and trajectory knowledge errors are significant impairments which may make ineffective the recombination algorithms since the error contributions are hard to estimate.In this paper, a Deep Learning based approach is proposed for performing the recombination of data coming from a swarm of satellites and where no a priori knowledge error is exploited. The proposed system provides promising results in terms of ambiguity rejection.

We discuss a coherent synthetic aperture radar (SAR) formation where$N$identical sensors transmit at the same time, code, and frequency. This is a particular multiple-input–multiple-output (MIMO) configuration, where the transmitted waveforms interfere together, resulting in an illumination pattern that randomly changes in space and time. Similar to the single-input–multiple-output (SIMO) formations, the diversity provided by the$N$receiver phase centers can be used to mitigate this interference and reduce the pulse repetition frequency (PRF) for achieving large swath coverage. The good point, in the MIMO case, is that the signal-to-noise ratio (SNR) gain of the system increases, theoretically, with the square of the number of elements. However, residual spurious sidelobes may appear as ghosts of the multiple illuminators. In practice, the power gain is to be optimized, together with ambiguity rejection, sidelobes, and azimuth resolution. The actual performances achievable by these formations in terms of impulse response function (IRF), SNR, and sensitivity to the precise positioning of the sensors are discussed theoretically and based on simulations.

The paper provides the main results of the SAOCOM-1B independent commissioning phase carried out by Aresys in the framework of a collaboration with Argentinian space agency CONAE. The reported results include: azimuth and elevation pointing, IRF assessment, geolocation accuracy and radiometric and polarimetric calibration.

A coherent formation of$N$identical SAR satellites is considered in this paper. These sensors transmit at the same time, code, and frequency with no synchronization whichever, but for the timing of the arrivals, creating a Multiple Input Multiple Output (MIMO) configuration. The contemporaneous transmission of N signals is expected to result in a reciprocal interference that leads to an illumination pattern that changes in space and time. In spite of this, the increased number of phase centres (w.r.t. single satellite and SIMO cases) can be used to mitigate the interference and to raise the SNR of the equivalent system. The principles of a recombination algorithm are summarized. In order to evaluate the effectiveness of the MIMO reconstruction and to fully characterize the IRF of a MIMO system from a quantitative point of view, new performance figures of merit are defined.

External calibration of satellite altimeters is commonly used to assess the errors introduced by the instrument when performing its measurements. The on-ground processing of the altimeter acquisitions over an external calibrator is a key step to assess these errors starting from the knowledge of the exact location of the external calibrator and its characteristics. In this letter, a novel on-ground processing is proposed for high pulse repetition frequency altimeters based on the fully focused synthetic-aperture radar (FF-SAR) concept, which relies on the capability of the instrument to preserve the coherency of the transmitted pulses for sufficiently long time. Starting from the FF-SAR processing, methods are here proposed to perform both absolute range calibration and datation calibration. The effectiveness and performance of the proposed approach are verified by processing the transponder acquisitions of CryoSat.

The BIOMASS mission gives completely new challenges in external calibration arising from the orbital pattern needed for the tomographic and Pol-InSAR phases of the mission, the strong effects of the ionosphere at P-band, and the lack of pre-existing P-band data except over very limited parts of the globe. Together these create problems that can only be solved by combining infrequent visits to instrumented calibration sites with systematic exploitation of the properties of distributed targets and targets of opportunity. Proposed approaches to performing radiometric and polarimetric calibration are described, together with meeting geolocation accuracy requirements.

In this paper, the full focused processing in the frequency domain for high Pulse Repetition Frequency (PRF) radar altimeter science data is proposed. Considering a radar altimeter as a Synthetic Aperture Radar (SAR) instrument operating in near nadir-looking geometry, a SAR frequency-domain focusing algorithm, namely Range-Doppler algorithm, is analyzed. Then a properly modified processor for radar altimeters is described and implemented. Simulation set-up using parameters from two missions (CryoSat and Sentinel-6) and a preliminary result from in-orbit CryoSat data confirm the effectiveness of the full focused processing in the frequency domain. The proposed approach, compared to the one formerly proposed in literature [1], is less complex and more computationally efficient.

In this paper, the synthetic aperture radar (SAR) calibration for low-frequency missions by means of stable point targets is presented. Calibration at low frequency involves the absolute radiometric calibration, the antenna pattern and pointing characterization and validation, and the distortion system parameters' estimation. The use of traditional instrumentation, such as a polarimetric active radar calibrator, a corner reflector, or an active transponder, may be costly and can reduce the time the instrument is used for operational acquisitions. The purpose of this paper is to evaluate the potentiality in calibration of point targets for which the radar cross section and the time stability have been characterized. Given a calibration site, once that a set of the stable point targets have been detected by the analysis of an interferometric stack of SAR acquisitions, they may be used as passive calibrators for the validation of radiometry, elevation antenna pattern, and pointing estimation. We show that, although less targets are expected to be found in P- or L-band than in C- or X-band, a sufficient amount (about 250 targets per acquisition) can provide an accuracy in antenna pattern estimation of about 0.04 dB, if the target accuracy is 0.1 dB at 1σ.

Synthetic Aperture Radar (SAR) systems are effective tools for many remote sensing applications. In particular, the modeling of the backscatter coefficient under various conditions is of interest in many scientific fields. Seasonal changes can have a considerable effect on the values of the reflectivity of a target, therefore these variations should be estimated to improve the accuracy of the models. In this paper a statistical analysis of radar backscatter coefficient seasonal variations using C-band SAR data is presented.

This paper concerns the on-board data volume reduction for a single-platform multichannel Synthetic Aperture Radar (SAR) system. In such a system, the unambiguous Doppler spectrum reconstructed on-ground is enlarged at the cost of an increased amount of acquired data. Since the signals received by a multichannel SAR instrument are intrinsically correlated, such correlation can be exploited to represent the signals in a smaller set of components, introducing a controlled distortion. This way, a reduced amount of data can be quantized and transmitted on-ground. Data simulations have been performed to evaluate the performance of the proposed approach. By analysis of the simulation results, efficient reduction of data volume is achieved, even keeping low enough the additional distortion introduced in the reconstructed data.

In this work we study the problem of focusing Synthetic Aperture Radar (SAR) images by means of Compressive Sensing (CS). The presented technique is introduced as an alternative to the traditional focusing methods, suggesting new modes for data acquisition in a more efficient configurations or on-board processing in case of spaceborne sensors. In this paper the method is tested on both simulated and real images acquired by a Ground Based Synthetic Aperture Radar (GB-SAR), for which images of reduced size can be generated with no difficulty. Results of comparison of CS processing with an exact focusing algorithm are shown in terms of root mean square error of amplitude and phase as a function of the number of focused targets and undersampling of the acquisition lines. Coherence of a CS-processed couple of images is also evaluated. The purpose of the paper is to show the potential of CS applied to SAR systems, regardless (at the moment) of the efficiency in computational load. In particular, we show that the image can be reconstructed without loss of resolution after dropping a fair percentage of the received pulses.

This paper concerns the performance of the Flexible Dynamic Block Adaptive Quantizer (FDBAQ), the on-board data compression scheme used by Sentinel-1A (S-1), the C-band Synthetic Aperture Radar (SAR) system for Earth Observation launched on 3rd April 2014. FDBAQ has been designed to deal with the high instrument data rate due to the high spatial resolution and large swath width. S-1 SAR is able to acquire long data take, corresponding to large volumes of data stored on-board. Since FD-BAQ has been implemented for the first time on S-1, the capabilities of such a compression scheme have not been tested on real data before. The purpose of this paper is to present the results of the analysis of real acquisitions acquired after the end of the Commissioning Phase, so in its full efficiency. FDBAQ is compared with the traditional Block Adaptive Quantizer (BAQ) compression scheme in terms of quantization noise level, average bit rate and signal to noise ratio.

Image mining consists of the procedures that allow to access, search and explore very large databases of data. Institutions like spatial agencies have to manage huge archives of Earth Observation (EO) images and need solutions to make data available to users from both the algorithmic and the infrastructural point of views. On the other side, users would need to explore the variety of images not just based on metadata, like time of acquisition or sensor parameters, but also by getting knowledge of their content. In this contribution, we investigate methodologies for content-based EO image retrieval via example-based queries. In particular, we present a procedure for the indexing of large-scale unstructured archives, built on top of a cluster analytics framework, Apache Spark. The procedure is based on a hierarchical and scalable implementation of a space partitioning algorithm and allows O(log n) response query times. Scalability analyses are conducted on polarimetric data from NASA/JPL archives, by using virtualized computing resources distributed over the Internet. In particular, the effects of the cluster size and of the hardware scale-up are demonstrated. The results also reveal the applicative potential of using on-demand cloud-based resources.

This paper concerns the performance of the flexible dynamic block adaptive quantizer (FDBAQ), which is the onboard data compression scheme used by Sentinel-1 (S-1), i.e., the C-band synthetic aperture radar (SAR) constellation whose first satellite (Sentinel-1A) has been launched on April 3, 2014. The data rate that results from the fine resolution and the wide swath of S-1 would exceed, without data compression, the S-1 hardware limitations. The FDBAQ has been proposed as an efficient method to reduce the instrument data rate and limit the onboard storage requirement. The capabilities of such a compression scheme have not been tested on real data before. Preflight simulation and analysis were based on a simplified processing scheme and radar backscattering information taken from past missions. The purpose of this paper is to present the first results of the analysis of real acquisitions, comprising thousands of products, in every S-1 acquisition mode, acquired during and after the commissioning phase. Advantages and limits are presented in detail, comparing FDBAQ and the traditional block adaptive quantizer (BAQ) compression scheme, in terms of quantization noise level, signal-to-noise ratio, and average bit and data rates.

This paper describes the principles and implementation of an algorithm for the classification of hyperspectral remote sensing images. The proposed approach is novel and can be included within the category of the spectral-spatial classification algorithms. The elements of novelty of the algorithm are as follows: (1) the implementation of two classifiers that work iteratively, each one exploiting the decision of the other to improve the training phase, and (2) the use of relational features based on the current labeling and on the spatial structure of the image. The two classifiers are fed with the spectral features and with the spatial features, respectively. The spatial features are built using the relative abundance of each class in a neighborhood of the pixel (homogeneity index), where the neighborhood is properly defined. An important contribution to the success of the method is the adoption of a multiclass classifier, the multinomial logistic regression, and a proper use of the posterior probabilities to infer the class labeling and build the relational data. The results of the two classifiers are eventually combined by means of an ensemble decision. The algorithm has been successfully tested on three standard hyperspectral images taken from the Airborne Visible-Infrared Imaging Spectrometer and ROSIS airborne sensors and compared with classification algorithms recently proposed in the literature.

This paper proposes a set of tests that provide necessary and, as far as possible, sufficient conditions for verifying the phase preservation of synthetic aperture radar (SAR) processors. This paper discusses two different test families composed of self-consistency and point-target-based tests. The set of self-consistent tests extends the well-known CEOS-OFFSET to very different acquisition modes, namely, StripMAP, ScanSAR, TopSAR, and SPOTLIGHT, and introduces a new complementary test to check the consistency of the space-variant implementation of a processor. The set of point target tests, based on the analysis of point target simulated data, completes the self-consistent tests by providing a verification of phase accuracy between different bursts and swaths.

This paper addresses the problem of estimating the azimuth antenna pattern by using a set of persistent point scatterers (PPS) retrieved from a stack of interferometric synthetic aperture radar images. This is achieved by means of a maximum likelihood estimation. PPS emerge as a restricted subset of the well known persistent scatterers, for which many applications have been described in the literature. PPS have a more stringent property since they explicitly require an impulsive trend feature; a good degree of isolation from the neighboring targets is further necessary to estimate the antenna pattern by means of digital spotlight focusing. A statistical model for PPS is provided and experimentally validated; the sufficient number of PPSs necessary to get a given accuracy for the azimuth antenna estimation is also suggested. Results using both simulated and real X-band Cosmo Skymed data are eventually illustrated.

This paper addresses the problem of characterize the statistical properties of Persistent Point Scatterers (PPS) retrieved in a stack of Interferometric Synthetic Aperture Radar (InSAR) images. The set of extracted parameters can be exploited to infer information on the radiometric response of targets to microwave excitation and then on their physical nature. Amplitude, Signal-to-Noise Ratio (SNR), dispersion index and antenna similarity index (a new defined parameter) are statistically characterized for different kind of land cover, from full urban to forested areas. A stack of images in X-band, taken by the Cosmo SkyMed constellation, are used to get PPS statistics.

Ground-Based Synthetic Aperture Radar (GB-SAR) is emerging as a relatively new system for specific short ranges and frequent monitoring applications. The main GB-SAR focusing algorithms proposed in literature are compared in terms of focusing quality and ability to manage the approximation of the space-varying two-dimensional kernel. Residual migration and phase errors which have implications on the target impulse response deformation, are given both in analytic and numerical forms, using simulations and real data acquired with a Ku-band system.

This work presents the result of an experiment aiming to derive high resolution Digital Elevation Models (DEMs) of areas spanning at most few squared kilometers by means of Ground-Based Synhetic Aperture Radar (GBSAR) interferometry. Spatially dense maps of topographic height are obtaned by exploiting pixels of SAR interferograms characterized by high values of coherence and applying kriging interpolation to generate regular-grid maps.

In this paper, we revise the relative radiometric calibration of synthetic-aperture-radar stacks which exploits natural persistent scatterers (PSs). We introduce a new model to estimate a slight error in the sensor pointing in elevation and a new coherent method that makes use of phases evaluated by averaging the complex data on the local window. We show that the proposed approach outperforms the conventional one, as it is mostly insensitive to the “differential” biasing that affects the noncoherent permanent scatterer calibration (PScal). Results from processing COnstellation of small Satellites for the Mediterranean basin Observation (COSMO)-SkyMed and European Remote Sensing (ERS) satellites stacks are presented.

The purpose of the paper is to estimate the Azimuth Antenna Pattern by using Persistent Point Scatterers (PPS) selected in a stack of interferometric Synthetic Aperture Radar images. Wide angle antenna pattern is estimated from targets spectra by means of a Maximum Likelihood formulation. PPS are considered a restricted subset of the Persistent Scatterers, for which the point-shape feature is further required. A model for PPS is provided and experimentally validated; antenna estimation results are presented for both simulated and real X-band Cosmo Skymed data.

We propose a novel method to select long-term coherent targets from a set of repeated-pass interferometric acquisitions. The major assumption is that targets are so close together to share the same optical path. This leads to an efficient singular-value-decomposition-based estimator of the targets' phase and, then, to PS detection. The estimated phase can be used to define the PS coherence for each image. Being sensitive to acquisition and processing artifacts, the PS coherence is a valuable tool for quality assessment of the interferometric stack. Results are shown with real data sets in both single and full polarizations.

In many real-time applications, such as wireless sensor network monitoring, traffic control or health monitoring systems, it is required to analyze continuous and unbounded geographically distributed streams of data (e.g. temperature or humidity measurements transmitted by sensors of weather stations). Storing and querying geo-referenced stream data poses specific challenges both in time (real-time processing) and in space (limited storage capacity). Summarization algorithms can be used to reduce the amount of data to be permanently stored into a data warehouse without losing information for further subsequent analysis. In this paper we present a framework in which data streams are seen as time-varying realizations of stochastic processes. Signal compression techniques, based on transformed domains, are applied and compared with a geometrical segmentation in terms of compression efficiency and accuracy in the subsequent reconstruction.

A novel approach is here presented to embed a low-priority data stream into a high-priority data stream for OFDM wireless communications. The main improvement with respect to conventional hierarchical modulations is that the low-priority data stream can be decoded from the received subcarrier symbols without any previous equalization in case of multipath fading channel. As a consequence, the expected performance is independent of the channel estimation method used to equalize the channel. The low-priority signal is superimposed on the high-priority symbols using a dirty paper coding technique. As a result of the analysis of the proposed method, several improvements to reduce the interference on the high-priority symbols are introduced. The performance of our scheme has been compared with that of a conventional hierarchical modulation using DVB-T system parameters, showing a considerable improvement for multipath fading channels.

The paper addresses the problem of finding an optimal estimation of the Doppler centroid for Synthetic Aperture Radar (SAR) data. The original idea is to exploit a bandwidth much wider than the PRF, say 3–5 times, by selecting non-aliased point targets. Non-aliased band of natural and isolated targets with close-to-ideal features is evidenced by spotlight processing of stripmap acquisitions and accurate Doppler centroid is estimated by means of a joint Maximum Likelihood (ML) estimator. Lower bound of the estimate is determined and results on both simulated and real X-band SAR data are shown.

We propose a robust and reasonably fast approach to provide phase estimation and stable target (Persistent Scatterers, PS) detection from a set of repeated pass interferometric acquisitions. The major assumption is that, locally, the PS phase model is decomposed into the separable product of the intrinsic target phase times the optical path, that is common to all the targets in the estimation window. The Singular Value Decomposition (SVD) is exploited to get at one time a fast estimate of the local phase and the detection of stable targets. The estimated phases are then exploited to measure the PS coherence over the whole image or in image blocks. Being sensible to acquisition and processing artifacts, the PS coherence is a valuable tool for automatic image quality assessment. Results are shown with real datasets in both single and full polarization.

A novel approach is here presented to superimpose a low-priority data stream on a high-priority data stream for OFDM systems. The main improvement with respect to conventional hierarchical modulation, which is included with the same purpose in various wireless technologies, is that the low-priority data stream can be decoded from the received subcarrier symbols without any previous equalization for multipath fading channels. As a consequence, the expected performance is nearly invariant of the channel estimation method used to equalize the channel. The low-priority stream is inserted adopting a gain-invariant dirty paper coding based on Rational Dither Modulation, which was proposed for data hiding applications. In this paper an analysis of the developed system is presented and several simulations have been carried out using DVB-T system parameters to verify the validity of the proposed approach. The experimental results show the better performance of the proposed method with respect to that of a conventional hierarchical modulation, particularly when the accuracy of the estimated channel response decreases.

A space adaptive flexible block quantizer (SA-FBQ), suited for spaceborne synthetic aperture radar missions, is presented. This quantizer is actually an extension of the flexible dynamic block adaptive quantizer (FDBAQ), proposed in, which, in turn, is an extension of the block adaptive quantizer (BAQ). The BAQ is the optimal quantizer for a homogeneous target. The FDBAQ gets better performances on heterogeneous targets by adaptively selecting the best BAQ according to the local signal-to-thermal-noise ratio: The worst the SNTR, the lower the quantizer rate. The quantizer selection is precomputed in a lookup table (LUT), by assuming a fixed and known probability distribution function (pdf) of the reflectivity$\sigma_{0}$. The SA-FBQ extends further this concept allowing the reflectivity pdf to vary, coping with this by exploiting many LUTs (i.e., quantizer set), each adapted to the local statistics. In this paper, we introduce an algorithm to adaptively find the best set of quantizers constrained on the mean bit rate; we discuss the implementation of the SA-FBQ, and we estimate its performances in comparison with the FDBAQ and the BAQ under different scenarios. Preliminary results are shown by exploiting the worldwide mosaic of C-band reflectivity derived from European Space Agency ENVISAT data and Sentinel-1 system parameters.

The letter introduces a novel quantizer suited for medium to high-resolution synthetic aperture radar (SAR) systems, like the forthcoming SENTINEL-1 SAR. The Flexible Dynamic Block Adaptive Quantization (FDBAQ) extends the concept of the Block Adaptive Quantization (BAQ), used in spaceborne SAR since the Magellan mission, by adaptively tuning the quantizer rate according to the local signal-to-noise-ratio (SNR). A design is presented aiming to optimize the average bit-rate, while constraining the minimum SNR. FDBAQ optimized performance is then evaluated using backscatter maps derived from ENVIronment SATellite (ENVISAT) data.

A novel framework providing invariance to a class of nonlinear valumetric distortions, such as gamma correction, for QIM-based watermarking techniques is presented. Valumetric distortions are quite common in image and video processings and the sensitivity to valumetric scalings represents the main weakness of the watermarking techniques belonging to the quantization based class. The proposed method amounts to perform a mapping of the host samples in a proper transformed domain where the watermark is subsequently embedded using a gain invariant QIM-based technique. The effectiveness of this approach has been verified by applying the watermarking system using RDM, AQIM and DM in the logarithmic domain as embedding algorithms. Simulation results provide a useful comparison of the performance of these different techniques within the proposed scheme as well as they confirm the invariance to nonlinear distortions.

Quantization-based watermarking techniques are sensitive to valumetric scaling, a wide class of distortions applied to images and videos, such as contrast change or gamma correction. Several methods have been proposed to counteract valumetric attacks, but the common approach to the problem is to take only the linear ones into account. This paper presents an extension to the rational dither modulation (RDM) data-hiding scheme which provides robustness against nonlinear distortions modelled by a power-law attack. The algorithm makes use of proper mapping of the pixel values from the Cartesian to hyperbolic coordinates. This mapping is able to render the problem similar to the classical RDM scheme, since fixed multiplicative scaling is cancelled out while the exponentiation of a nonlinear distortion is transformed into a gain scaling. The validity of the approach has been confirmed by applying the watermarking scheme to Gaussian host and real images; experimental results confirm its intrinsic invariance against the power-law attack. Finally, it will be shown that under the white Gaussian noise addition, the proposed scheme achieves a good bit-error rate (BER). The measured BER is affected by the properties of the embedding domain, as supported by the theoretical analysis given in this paper.

This paper presents a motion compensation algorithm to focus high resolution SAR data taken from an airborne sensor. The basic idea is to fragment the sensor trajectory into smaller segments that can be modelled as linear or quadratic. The synthetic antenna is then considered as an array of smaller subarrays. The algorithm can easily adapt and trade efficiency for sensor path irregularity. In fact the algorithm is more efficient if the segments are longer. Experimental results with real airborne data confirm the correctness of the approach.

The use of a radar altimeter is to measure the height of the reflecting surface when the instrument passes overhead. A delay/Doppler altimeter (DDA) reduces the along-track resolution cell using correlation of pulses within burst, as in a synthetic aperture radar, with the net effect of synthesizing an array with a narrower lobe. The processing of DDA data therefore implies the correction of the phase delay of pulses, which is said to beDopplerbeamsharpeningsince it can be reduced to a DFT in the along-track direction. The noise term on Doppler echoes is decreased by incoherent summing of contributions of different bursts. Summation of these contributions requires that pointing of each beam is directed toward a given output grid of samples on ground, and this can be a nontrivial task, depending on the topography profile. In this letter, a comparison between different beam sharpening algorithms is presented: Two of them are similar in their basic idea even if with different implementations; the third one can be considered as reference since no approximations are needed in principle, but is computationally inefficient. What results is that the first couple can be considered basically equivalent in terms of precision and computational efficiency, with little differences at the change of the environmental conditions. Results based on simulated data of simple and complex scenarios are presented as support of the reasoning.

The main goal of a satellite radar altimeter is to measure the height of the reflecting surface scanned by the passage of the instrument overhead. A delay/Doppler altimeter reduces the along-track footprint size by exploiting the coherence of the emitted pulses to synthetize a narrower antenna. Doppler beam formation is then a straightforward procedure that implies application of the discrete Fourier transform to time-domain bursts. Speckle and thermal noise on Doppler echoes are usually reduced using multilooking, i.e., summing incoherent contributions coming from different bursts. The summation of Doppler beams requires precise pointing toward output sample locations, particularly when highly variable topography is effective. However, the procedure of steering after beam sharpening needs a step of interpolation. In this paper, a Doppler beam sharpening and direction toward the sample position are presented which make use of the Chirp Zeta Transform to join the two operations together. This method must be compared with the existing ones to show that a little can be sacrificed to precision for computational gain. To support the discussion, experimental results will be compared with a precise-and so of reference-direct 2-D correlation algorithm over puntiform targets and more complex scenarios.

The ESA Cryosat-2 mission will mount a de- lay/Doppler radar altimeter (DDA), named SIRAL, for the study of the trends in Earth's continental and marine ice fields. A DDA has many advantages over a conventional altimeter, retrieving a better resolution in the along track direction as a result of coherent processing of the backscattering energy. This permits the extension of applications to ice sheet monitoring as well as coastal studies, maintaining necessary precision for the open ocean. Operational SIRAL data processing for the Cryosat-2 mission is based on the precise wavenumber domain approach, following the strategy developed and verified for the Cryosat-1 mission [1]. A novel way of processing, supported by the chirp zeta transform (CZT) is here presented. Basically the CZT allows the Doppler beam formation to be directed toward the output surface samples in a single stage, increasing the computational efficiency of the processing at the expense of a slightly lower accuracy. The more precise wavenumber domain approach can be used to validate the method. The SIRAL sensor parameters and simulated scenarios will be taken as models to derive experimental results.

The possibility to get efficient topographic mapping and monitoring of large-scale motions with ScanSAR interferometry has been demonstrated with the Shuttle Radar Topography Mission and RADARSAT mission. The Environmental Satellite Advanced Synthetic Aperture Radar (ASAR) sensor has been designed to provide enhanced capabilities for interferometric applications. Different types of interferometric products can be obtained by combining the various ASAR modes as stripmap synthetic aperture radar [image mode (IM)] and ScanSAR [wide swath (WS) mode]. This letter deals with the possibility to use WS data to get either mixed-mode (IM/WS) or ScanSAR mode (WS/WS) differential interferograms. The impact of digital elevation model localization errors on IM/WS interferograms and of scan pattern synchronization on WS/WS interferograms is investigated. Experimental results are encouraging and show that ASAR ScanSAR data can be routinely used for interferometric applications in both cases

We introduce a novel accurate technique to estimate the Doppler centroid (DC) in ScanSAR missions. The technique starts from the ambiguous DC measures in the subswaths and uses a method alternative to standard unwrapping to undo the jumps in estimates induced by modulo pulse repetition frequency (PRF) measures. The proposed alternative is less error prone than the usual unwrapping techniques. Doppler Ambiguity is then solved by implementing a maximum-likelihood estimate that exploits the different PRFs used in different subswaths. An azimuth pointing of the antenna that does not change with subswaths, or that changes in a known way, is assumed. However, if the PRF diversity is strong enough, unknown small changes in azimuth pointing are tolerated and accurately estimated. This estimator is much simpler and more efficient, than those in the literature. Results achieved with both RADARSAT 1 and ENVISAT ScanSAR data are reported.

We summarize a technique capable to combine SAR acquisitions in different modes, like scanSAR/scanSAR or scanSAR/SAR, and we presents preliminary results achieved by processing early ENVISAT acquisitions in AP and WSM mode.

Deals with the focusing of low resolution ScanSAR data, for both detected amplitude images and interferometric applications. The SAR reference is exploited to achieve ScanSAR focusing in conventional techniques. Such techniques provide quite effective compensation of the azimuth antenna pattern (e.g. no scalloping) when the azimuth time-bandwidth product of the ScanSAR echo is large, but fail to do so as the burst shortens, being reduced to an ineffective weighting of the output. The result is an azimuth varying distortion of the focused impulse responses, a distortion that is partly compensated for in the multilook average (not available for interferometric applications) at the price of a reduction in the processed Doppler bandwidth. This paper proposes quite a different approach. A set of short kernels, each suitable for "focusing" at a specific azimuth bin, has been optimized to reconstruct source reflectivity in the minimum mean square error sense. That pseudoinversion converges to the "conventional" focusing when the burst extent is large and for short bursts, edge effects are accounted for. These azimuth-varying kernels can be suitably tuned to meet constraints in the resolution/sidelobes trade-off and have proved capable of providing fairly undistorted output and fine resolution. They better exploit the available Doppler bandwidth, maximizing the number of looks and the interferometric quality. A decomposition is suggested that implements the inverse operator as a fast preprocessing to be followed by a conventional ScanSAR processor.