We here analytically evaluate the spatial correlation coefficient of an interferometric pair obtained by combining two bistatic SAR acquisitions. The scattering surface is described as randomly rough, and the Kirchhoff Approximation (KA) or the first-order Small Slope Approximation (SSA1) are used to compute scattered fields. Both approximations lead to the same expression of the correlation coefficient, that generalizes to the bistatic case, with arbitrary acquisition geometry, the result available in literature for usual monostatic SAR interferometry.

This paper is aimed at supporting the analysis and interpretation of bistatic synthetic aperture radar (SAR) images, which are available by now through the Capella constellation or will be soon available thanks to the upcoming PLATiNO-1 mission. More specifically, here we focus on urban areas, where the so-called cardinal effect dominates the backscattering in conventional monostatic SAR imagery. Such effect is also evident in bistatic acquisition geometries, as it has been recently demonstrated, even if with different dynamics. Through the application of analytical models for the electromagnetic scattering from buildings in bistatic geometries, we provide a physical explanation of the SAR image formation in the urban environment and in arbitrary acquisition geometries. We quantitatively evaluate the contrast between the returns from the building and the surrounding terrain under different roughness regimes and imaging geometries. The analyses provided here might support a correct human interpretation and automatic processing of urban bistatic SAR images.

In the context of bistatic synthetic aperture radar (SAR) imaging, SAR interferometry is an appealing application due to the capability of retrieving accurate topographic information or surface deformations at fractions of wavelength. Within this framework, we present a new physical-based approach to evaluate the correlation between a pair of bistatic SAR acquisitions over a bare soil surface and in a very general imaging geometry, which includes two transmitters and two receivers. Some specific configurations of practical interest for proposed bistatic spaceborne SAR missions, for example, SESAME and PLATiNO-1 (PLT-1), namely coplanar and along-track bistatic geometries, are analyzed as well. The proposed methodology makes use of electromagnetic scattering models suited to random rough surfaces, namely the Kirchhoff approximation (KA) and the first-order small-slope approximation (SSA1), under which analytical formulations of the correlation between the received electromagnetic fields are derived. It is found that in the coplanar imaging geometry, a unitary correlation coefficient can be obtained with nonnull orthogonal baselines. Closed-form expressions of the critical baseline are derived as well. The proposed approach can be applied to such scenarios where single surface scattering is the dominant mechanism, such as bare soil surfaces or scarcely to moderately vegetated areas.

Models linking the electromagnetic field scattered from the sea surface along the specular direction to the speed of the wind blowing over the surface are of fundamental importance for wind speed retrieval via Global Navigation Satellite System Reflectometry (GNSS-R). In this work, by modelling the sea surface as a fractional Brownian motion (fBm) random process and using the Kirchhoff approximation (KA), we express the sea bistatic normalized radar cross section (NRCS) σ0at specular direction directly in terms of sea surface spectrum parameters, and hence of wind speed. This avoids the need of intermediately computing the large-scale sea surface slope variance, which in turn would require the definition of a somewhat arbitrary cut-off surface wavenumber. We show that the obtained theoretical relationship between wind speed and σ0is in reasonable agreement with the empirical ones available in literature.

Interpretation and processing of synthetic aperture radar (SAR) imagery is negatively affected by speckle noise, that can be faced by proper despeckling pre-processing. In this work, we propose a simple approach for the despeckling of multi-frequency SAR data under the hypotheses that they are relevant to bare soil surfaces and that are acquired by the same sensor, e.g., AIRSAR. The proposed approach is based on a frequency-compensation step, where the dependence of the signal strength upon operating frequency is compensated by means of surface scattering model suited to natural surfaces. Once such a pre-processing step is carried out, any despeckling filter suited to SAR time series filtering can be applied. Quantitative indicators evaluated on both simulated and actual SAR data reveal the benefits of the proposed compensation step w.r.t. pure multitemporal and single-channel filtering.

In the framework of passive multistatic radars, formation-flying synthetic aperture radar (FF-SAR) represents an intriguing remote sensing solution due to the enhanced imaging capabilities with respect to the conventional SAR. In this article, we focus on FF-SAR systems operating in a far-from-transmitter geometry, developing a signal model suited to this specific configuration. Additionally, we present an efficient processing scheme that, by properly combining the received raw echoes in a coherent fashion, enables two peculiar imaging modes of FF-SAR, namely, signal-to-noise ratio (SNR) improvement and high-resolution wide swath (HRWS). Simulation results show that achieved radiometric and geometric imaging performances are in line with those offered by an equivalent monostatic SAR. In HRWS imaging mode, azimuth ambiguity suppression and SNR improve as the number of satellites increases and are maximized by specific receivers along-track baselines, whose expression is provided. Finally, we present a statistical analysis of the processing performance parameters for random receivers’ positions, both assuming a fixed pulse repetition frequency (PRF) and allowing for an adaptive PRF tuning. This last analysis is also directly applicable to clusters of SAR receivers not far from the transmitter.

In this article, we propose a novel benchmarking framework for a quantitative assessment of the performance of despeckling algorithms for multitemporal synthetic aperture radar (SAR) imagery. A number of canonical scenes and data sets are analyzed so as to investigate both speckle reduction and feature preservation capabilities of the filters. Despeckling performance is evaluated by proper quality measures that are defined according to the scene. Due to the lack of real-world speckle-free SAR images, the proposed benchmarking tool relies on an accurate and well-assessed SAR simulator which allows for generating realistic SAR images accounting for electromagnetic (EM) and geometrical parameters of the sensed surface. Accuracy and convergence properties of filters are first measured on scenes with stationary reflectivity. Then, for a more realistic performance prediction in practical situations, the effects of temporal changes of the scene reflectivity on the despeckled images are measured on time series with time-varying reflectivity. In the latter case, performance parameters are intended to measure the capability of the filter to preserve both the perturbation and its impact on the other bands. The whole benchmarking framework is applied to a representative set of state-of-the-art multitemporal filters. Interestingly, their performance as evaluated by means of our framework is well in agreement with (qualitative) visual inspections by SAR specialists. Proposed quality metrics are measured under the hypothesis of uncorrelated bands, which defines the best case for most multitemporal filters. A numerical sensitivity analysis of the performance of filters against correlation coefficient is carried out to investigate the temporal correlation effects on the despeckled time series.

The paper focuses on the concept of a formation-flying synthetic aperture radar (FF-SAR) bistatic system composed of a set of compact, low-weight satellite receivers in close formation (within 1 km) placed in the same low-Earth orbit at large distance (about 100 km) from a transmitter. Each receiver is conceived to fit a 12-unit CubeSat. A signal model adapted to the proposed formation geometry is also presented, and a corresponding processing scheme to achieve range swath widening and signal-to-noise ratio (SNR) improvement is illustrated and discussed.

This paper discusses a new receiving antenna for remote sensing applications to be mounted onboard a formation-flying synthetic aperture radar (FF-SAR) bistatic system based on the CubeSat standard. The formation works as a bistatic SAR collecting microwave signals coming from a transmitting SAR unit. The receiving antenna has been designed according to the acquisition modes of the formation, namely a stripmap mode for signal-to-noise ratio improvement and a High-Resolution Wide-Swath mode for the monitoring of large regions. In order to meet the very different requirements for both operating modes with the physical constraints imposed by the nanosat geometry, a large reflector with reconfigurable feed has been conceived and simulated. The results show that the proposed antenna accomplishes the design specifications.

In this paper, we provide a link budget analysis of the Global Navigation Satellite System-Reflectometry (GNSS-R) signal scattered off the sea surface in arbitrary viewing geometries. The study is based on a recent electromagnetic scattering two-scale model for rough surfaces, named BA-PTSM, which is able to accurately describe the microwave scattering from sea surface at any acquisition geometry, including backscattering and forward-scattering, and on a simulator that accounts for geometrical and system parameters. Comparisons with Geometrical Optics (GO), whose validity is limited to specular and quasi-specular scattering, demonstrate that a reliable simulation of GNSS-R signals in configurations other than the conventional forward-scattering one calls for scattering models more accurate than GO, e.g., BA-PTSM.

Bistatic radars have been a topic of increasing interest in recent years, thanks to the introduction of new bistatic (and multistatic) configurations, including those based on the opportunistic exploitation of global navigation satellite systems (GNSSs). The research on bistatic electromagnetic scattering models plays an important role in the analysis of these systems, in their simulation, and in the prediction of their performance. The two-scale model (TSM) is a widely used approach for the computation of scattering from rough surfaces, since it is able to account for depolarization effects due to surface tilting. However, in its original formulation, it requires a computationally intensive numerical integration, in order to perform appropriate average over surface random slopes. To overcome this limitation, a closed-form polarimetric TSM (PTSM) was developed, which has been also recently extended to the case of anisotropic rough surfaces (A-PTSM), with a focus on the sea surface. The A-PTSM can be efficiently used to compute the backscattering from anisotropic rough surfaces and can support the development and analysis of monostatic radar missions. In order to extend its scope to the general case of bistatic and multistatic configurations, in this article, we extend the A-PTSM to the case of bistatic electromagnetic scattering, presenting the evaluation of all the elements of the bistatic polarimetric covariance matrix. Due to the relevance of circularly polarized signals in opportunistic GNSS reflectometry applications, both the linear and the circular polarization bases are considered. The behavior of the obtained elements is discussed, and simplified expressions of the elements of the covariance matrix are provided for the case of scattering within the incidence plane. Relevant numerical examples are provided and compared to those obtained by the more refined, but more computationally intensive, second-order small-slope approximation (SSA2) method. In the examples, we consider both a wind-driven sea surface and a tilled soil, and both L-band and X-band frequencies. However, the presented method can be used at all frequencies of interest for microwave remote sensing and for all observation geometries, except for near grazing incidence and/or scattering.

As sadly known, forest fires are part of a set of natural disasters that have always affected regions of the world typically characterized by a tropical climate with long periods of drought. However, due to climate changes of the recent years, other regions of our planet that were not affected by this plague have also had to deal with this phenomenon. One of them is certainly the Italian peninsula, and especially the regions of southern Italy. For this reason, the scientific community, and in particular that one of the remote sensing, plays an important role in the development of reliable techniques to provide useful support to the competent authorities. Therefore, in this work, the capability of the Normalized Differential Water Index (NDWI), derived from spaceborne remote sensing (RS) data, is assessed to monitor the forest fires occurred on a specific study area during the summer of 2017: the volcano Vesuvius, near Naples (in Campania, Italy). In particular, the index is obtained from Sentinel-2 multispectral images of the European Space Agency (ESA), which are free of charge and open accessible. Moreover, the twin Sentinel-2 (S-2) sensors allows to overcome some restrictions on time delivery and high frequency observation. These requirements are goodly matched by other spaceborne sensors, such as MODIS and VIIRS satellites, but at the expense of a lower spatial resolution.

The Two-Scale Model (TSM) is a useful approach for the evaluation of scattering from rough surfaces. In order to model depolarization effects, TSM, in its original formulation, requires an average operation that calls for numerical integration, so strongly reducing the computation efficiency. However, in the last decade a method to analytically compute this average in closed form, named Polarimetric TSM (PTSM), was devised. More recently, the PTSM, originally applied to backscattering from statistically isotropic rough scattering surfaces, was extended to the case of anisotropic rough surfaces (A-PTSM), in order to compute backscattering from sea surfaces. Both PTSM and A-PTSM, as presented in previous work, only consider the monostatic configuration. Here, we extend A-PTSM to the more general case of bistatic scattering. In particular, we present the procedure to obtain all the elements of the bistatic polarimetric covariance matrix in closed form; we then present some numerical results with reference to the bistatic Normalized Radar Cross Sections (NRCS) in circular polarization basis, which are of interest for some recently proposed bistatic passive radar systems with GNSS signals of opportunity.

In this work we present a novel benchmarking framework for assessing the quality of despeckled multitemporal SAR images on an objective basis. Taking cues from a recent work providing a useful tool for measuring the performance of single-channel despeckling filters, here we extend that analysis to multitemporal filters and datasets. Due to the lack of reference speckle-free real SAR images, the quantitative performance parameters introduced here are evaluated on simulated time series of canonical scenes obtained through a reliable and well-assessed SAR simulator which accounts for both geometrical and electromagnetic properties of the scattering surface. First we analyze image quality over datasets with homogeneous reflectivity in time. Then, for a more realistic performance assessment in practical situations, ad-hoc quality parameters are introduced to measure the effects of time-varying scene characteristics on the despeckled dataset. The consistency of the proposed framework is tested on state-of-the-art multitemporal despeckling algorithms.

In this article, we present a fully analytical model for the evaluation of the electromagnetic (EM) field scattered from a composite target in a generic bistatic configuration. The scenario comprises a rectangular parallelepiped target with smooth dielectric faces lying over a rough background surface, modeled as a stochastic process. The single- and multiple-bounce scattering contributions arising from the target, the rough background, and their interactions have been derived under the Kirchhoff approximation (KA)-geometrical optics (GO) solution. This framework enables the evaluation of the bistatic radar cross section (RCS) of the considered composite target via closed-form expressions. The proposed model exhibits good agreement with the literature results based on accurate and well-established numerical methods. Our analytical model is therefore proposed as a valid alternative to numerical techniques, being able to provide reliable results at a negligible computational burden. Finally, the role of the main scene parameters, i.e., target orientation, surface roughness, and polarization in the bistatic RCS of the target, have been analyzed and discussed.

This paper aims at developing a new enhanced algorithm for mapping semi-arid areas based on fusion techniques of Synthetic Aperture Radar (SAR) and Light Detection And Ranging (LIDAR) datasets. Firstly, both datasets are preprocessed to remove geometric and radiometric errors; then features of interest are extracted from SAR and LiDAR products to build masks and identify meaningful classes. Finally, classification results are refined with morphological filters. The new algorithm has been tested on data acquired by TerraSAR-X and an airborne LiDAR sensor over the Natural Reserve of Maspalomas in Canary Islands. Results show an overall classification accuracy of 85% with an absolute increment of more than 14% compared to a classification in which only LiDAR data are used.

The polarimetric two-scale model (PTSM) was introduced a few years ago as an electromagnetic scattering model to be used within algorithms for soil moisture retrieval from polarimetric synthetic aperture radar (SAR) data. PTSM inherits the ability to account for depolarization effects from the original two-scale model (TSM), and, with respect to the latter, it has the advantage to provide closed-form expressions of the elements of the covariance matrix that hold for moderate large-scale surface slopes. This allows a very fast evaluation of scattering, since numerical integration needed by the original TSM is avoided. The TSM, also called composite model (CM), has been extensively used to study scattering from the sea surface, so that it is natural to explore the use of PTSM for the same purpose. However, in its current formulation, PTSM assumes that large-scale surface slope distribution and small-scale roughness spectrum are isotropic. This is not realistic for the sea surface, for which anisotropy is dictated by the wind direction. Accordingly, we here extend PTSM to account for surface roughness anisotropy, so obtaining the anisotropic PTSM (A-PTSM). In addition, as a second contribution, we provide A-PTSM expressions also in the circular polarization basis, which may be useful for some SAR sensor polarimetric configurations. Finally, we compare A-PTSM results with sea surface scattering measurements available in the literature and with results of the second-order small-slope approximation (SSA2). In particular, as a third original contribution of this paper, an analytical closed-form expression of the ratio of crosspolarized normalized radar cross sections (NRCSs) obtained by SSA2 and A-PTSM is provided.

In this work, we present a new architecture for the analysis multitemporal SAR data combining classic synthetic aperture radar processing and geographical object-based image analysis. The architecture exploits the characteristics of the recently introduced RGB products of the Level-1α and Level-1β families, employing self-organizing map clustering and object-based image analysis aiming at the definition of opportune layers measuring scattering and geometric properties of candidate objects to classify. The obtained results have been compared with those given by literature and turned out to provide high degree of accuracy and negligible false alarms. The discussion is supported by an example concerning small reservoir mapping in semi-arid environment.

In this paper, we present a novel method for mapping flooded areas exploiting Sentinel-1 ground range detected products. The work introduces two novelties. As first, the input products. In fact, as far we know, no applications using these products has been so far presented in literature. Secondly, a new unsupervised methodology, based on the usage of opportune layers combined in a fuzzy decision system, is presented. Experimental results, obtained both on the single SAR image and on a couple of acquisitions in a change detection framework showed that our method is able to outperform the most popular classification techniques in terms of standard assessment parameters.

Ocean GNSS-R is successfully being employed to retrieve wind speed from GNSS signals scattered by the sea surface. To obtain a sufficient scattered field intensity from the ocean surface, the receiver acquires data when it is located along the specular reflection direction. However, new applications of GNSS-R are being explored, among which ship-detection. In this case, scattering from the ocean represents the clutter to be suppressed, so that a different geometry, where the GNSS signals are received in backscattering configuration, is preferable to the forward scattering one. In this new geometry, the Geometric Optics, usually employed in the GNSS-R scientific community, is often no more appropriate to model scattering from the sea surface, and different scattering models must be used. To this aim, we here introduce the Polarimetric Two-Scale Model to evaluate the intensity of the GNSS backscattered signal.

Synthetic Aperture Radar (SAR) systems are active sensors able to generate microwave images by using different acquisition modes. The classical one is the stripmap mode, the other ones are chosen based on the trade-off between spatial resolution and coverage. In this paper, we will present a unified formulation able to express raw signals for all acquisition modes and we will employ it into an approach to show that both sliding spotlight and TOPSAR raw signals of extended scenes can be simulated. This approach consists of a 1D range Fourier-domain (1D-FD) processing, followed by a 1D azimuth time-domain (TD) integration, and it can also take into account for sensor trajectory deviations. Numerical examples are then shown in order to verify the effectiveness of the simulation scheme.

Global Navigation Satellite System-Reflectometry (GNSS-R), is succesfully employed for ocean altimetric and scatterometric applications. Recently, it has been suggested that GNSS-R can also be used for ship detection applications. To this purpose, an accurate electromagnetic modeling of the bistatic radar cross section of a ship lying over the sea surface would be very helpful. However, existing models are typically limited to monostatic configurations, thus restricting their applicability in multistatic scenarios, such as GNSS-R systems. In this work, we show a procedure to determine the bistatic radar cross section of a ship target, under the geometrical optics approximation. Numerical results show the impact of the geometry of acquisition and polarization on the bistatic radar cross section.

Spacebome sensors allow large areas of the Earth to be monitored with small revisit times; conversely, use of in situ techniques frequently limits spatial and/or temporal coverage of critical infrastructure observations. In this paper, some of the potentialities of synthetic aperture radar (SAR) for railway bridge monitoring are explored. The study is focused on a case study regarding a bridge near Triflisco, a small city in Campania, Italy. Twenty-six Cosmo-SkyMed stripmap SAR images were acquired over the area, allowing for a permanent-scatterer (PS) analysis of the bridge structure. Thanks to the availability of a detailed ground truth, the physical nature of the bridge PSs, their spatial distribution, and their time behavior are investigated. In particular, the PS behavior is compared to deformation results obtained using thermal loads in a finite-element model of the bridge.

In this paper, a comparative study of spaceborne Global Navigation Satellite System (GNSS)-Reflectometry for ship detection applications is provided. The analysis is conducted by evaluating the impact of 1) the acquisition geometry and 2) the received signal polarization on ship detectability in GNSS-R data. In particular, the backscattering acquisition geometry is demonstrated to be more suitable for ship detection applications, thus allowing for the detection of 20 m-length ships. Even very large ships are hardly detectable in the conventional forward-scattering geometry. Moreover, receiving right-hand circular polarization is demonstrated to provide significant improvements of the signal-to-noise-plus-clutter with respect to the conventional left-hand circular polarization channel, conventionally exploited in GNSS-R remote sensing. The study is based on a numerical tool for the bistatic radar cross section of the ship, which is presented in a companion paper.

We present a new methodology for rapid flood mapping exploiting Sentinel-1 synthetic aperture radar data. In particular, we propose the usage of ground range detected (GRD) images, i.e., preprocessed products made available by the European Space Agency, which can be quickly treated for information extraction through simple and poorly demanding algorithms. The proposed framework is based on two processing levels providing event maps with increasing resolution. The first level exploits classic co-occurrence texture measures combined with amplitude information in a fuzzy classification system avoiding the critical step of thresholding. The second level consists of a change-detection approach applied to the full resolution GRD product. The discussion is supported by several experiments demonstrating the potentiality of the proposed methodology, which is particularly oriented toward the end-user community.

Synthetic aperture radar (SAR) systems can generate microwave images by using different acquisition modes: stripmap, spotlight, scanSAR, and the more recently developed sliding spotlight and Terrain Observation by Progressive Scans (TOPSAR). The proper mode to be used is chosen according to the desired spatial resolution and coverage. In this paper, we present a unified formulation able to express raw signals of all acquisition modes. This formulation is then employed to show that both sliding spotlight and TOPSAR raw signal simulation of extended scenes can be achieved by using an improved version of the approach previously proposed by some of the authors for the sliding spotlight case. This approach implies a 1-D range Fourier-domain processing, followed by 1-D azimuth time-domain integration, and it can also precisely account for sensor trajectory deviations for any acquisition mode. Effectiveness of the proposed simulation scheme is assessed by using numerical examples. Results show that its computational load is much lower than the one of the time-domain approaches, and the obtained raw signals are in very good agreement with the exact ones. Finally, examples of simulations of SAR signals relative to extended, both canonical and realistic, scenes are also reported.

We present a new synthetic aperture radar (SAR) raw signal simulator, which is able to simultaneously generate raw signals of different polarimetric channels of a polarimetric SAR system in such a way that a correct covariance matrix is obtained for the final images. Extended natural scenes, dominated by surface scattering, are considered. A fast Fourier-domain approach is used for the generation of raw signals. Presentation of theory is supplemented by meaningful experimental results, including a comparison of simulations with real polarimetric scattering data.

We present a new Synthetic Aperture Radar (SAR) raw signal simulator, which is able to simultaneously generate the raw signals of the different polarimetric channels of a polarimetric SAR system in such a way that the correct covariance matrix is obtained for the final images. Extended natural scenes, dominated by surface scattering, are considered. A fast Fourier-domain approach is used for the generation of raw signals. Presentation of theory is supplemented by meaningful experimental results.

We present a model-based approach aimed to help predicting which objects in an imaged scene will appear as persistent scatterers in Differential Synthetic Aperture Radar Interferometry (DInSAR) data, given their shapes and sizes, and given the scene background and sensor parameters. The approach consists in computing the interferometric coherence of a pair of acquisitions for a resolution cell comprising a strong scatterer (trihedral or dihedral corner reflector, or metallic vertical cylinder) and a distributed scatterer (rough surface, vegetation).

Synthetic aperture radar (SAR) image interpretation and analysis are heavily affected by the speckle noise typical of coherent acquisition systems. Very recently, a novel despeckling approach based on the exploitation of a priori information about the scattering behavior of the illuminated surface has been presented. The new scattering-based algorithms, named SB-PPB and SB-SARBM3D, represent a scattering-oriented version of the preexisting PPB and SARBM3D algorithms. In this paper, a comparative sensitivity analysis of the abovementioned despeckling algorithms is performed by means of SAR image simulation tools. First, the sensitivity of the filters against the use of different electromagnetic scattering models for the illuminated surface is analyzed. Then, the roles of the digital elevation model resolution and of the coregistration step are also investigated.

Ships and ice monitoring is of key importance in numerous applications, such as maritime traffic control, prevention of illegal activities, climate change studies, and maritime security. In this work, the feasibility of near real-time ocean target detection from a constellation of spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) images is demonstrated by addressing the two following points: 1) simulation of the revisit time allowed by constellations of GNSS-R instruments; 2) derivation and implementation of an ocean target detector aimed at identifying inhomogeneous features inside the delay-Doppler map. The revisit time is computed by simulating a realistic mission as a function of the constellation size and the number of receiving channels of the GNSS-R instrument. The proposed ocean target detection algorithm consists of four steps — pre-processing, pre-screening, selection, and geolocation — and is validated using actual U. K. TechDemoSat-1 data.

In this paper, we present a new framework for the fusion, representation, and analysis of multitemporal synthetic aperture radar (SAR) data. It leads to the definition of a new class of products representing an intermediate level between the classic Level-1 and Level-2 products. The proposed Level-1β products are particularly oriented toward nonexpert users. In fact, their principal characteristics are the interpretability and the suitability to be processed with standard algorithms. The main innovation of this paper is the design of a suitable RGB representation of data aiming to enhance the information content of the time-series. The physical rationale of the products is presented through examples, in which we show their robustness with respect to sensor, acquisition mode, and geographic area. A discussion about the suitability of the proposed products with Sentinel-1 imagery is also provided, showing the full compatibility with data acquired by the new European Space Agency sensor. Finally, we propose two applications based on the use of Kohonen's self-organizing maps dealing with classification problems.

Recently, we have proposed a retrieval technique based on an original polarimetric two-scale model (PTSM), which is able to estimate the volumetric water content of bare soils from polarimetric synthetic aperture radar (SAR) data. In this paper, to extend the field of application of our retrieval technique to moderately vegetated soils, we combine the PTSM with a randomly oriented dipole-cloud volumetric scattering model, thus obtaining a polarimetric two-scale two-component model (PTSTCM). By using this model we show that, in principle, suitable combinations of the polarimetric SAR channels, i.e., “modified copolarized ratio” and “modified copolarized correlation coefficient,” are related only to the surface parameters because the dependence on the unknown volumetric contribution intensity cancels out. This allows us to retrieve soil moisture from L-band SAR data not only for bare soils but also in moderately vegetated areas, interested by a nonnegligible volumetric scattering contribution, provided that the double-bounce scattering component is negligible. In addition, describing the surface component by using the PTSM allows us to mitigate the well-known problem of overestimating the volume component, which affects most model-based target decompositions and that may lead to the so-called “negative power problem.” Both the performance and validity limits of the estimation method are assessed by comparing the obtained soil-moisture retrieval results to “in situ” measurements. To this aim, data from SMEX'03 and AGRISAR'06 campaigns available in literature are considered. They refer to sites with a flat topography. In particular, we employ the AGRISAR database, which includes data from several fields covering a period that spans all the phases of vegetation growth, to explore the validity range of the method in terms of vegetation height. Results of PTSTCM are also compared with those of available three-component methods (3CMs) employing more simplified surface scattering models. It has turned out that the use of the PTSTCM provides more accurate results for low vegetation (average modulus of soil moisture relative error for vegetation height smaller than 50 cm: 18.5% for the PTSTCM and 34% for the 3CM). Conversely, for higher vegetation, 3CMs should be more conveniently employed (average modulus of relative error for vegetation height greater than 50 cm: 17.5% for the 3CM and about 100% for the PTSTCM). A simple method to adaptively and automatically (i.e., based on measured data) select between PTSTCM and 3CM on a pixel-by-pixel basis is finally suggested, leading to a less than 20% average modulus of relative error on the retrieved soil moisture for the considered fields over the entire vegetation growth cycle.

Speckle noise greatly limits both synthetic aperture radar (SAR) data human readability, especially for non-SAR-expert users, and performance of automatic processing and information retrieval procedures by computer programs. Therefore, despeckling of SAR images is an essential preprocessing step in SAR data analysis, processing, and modeling, as well as in information retrieval and inversion procedures. Up to now, one of the most accurate and promising despeckling approaches - among those based on a single SAR image - is the one relying on the nonlocal means concepts. However, at the best of our knowledge, most of the state of the art considers the despeckling problem only within a statistical framework, completely discarding the electromagnetic phenomena behind SAR imagery formation. In this paper, we introduce the novel idea of a physical-based despeckling, taking into account meaningful physical characteristics of the imaged scenes. This idea is realized via the implementation of a physical-oriented probabilistic patch-based (PPB) filter based on a priori knowledge of the underlying topography and analytical scattering models. This filter is suitable for SAR images of natural scenes presenting a significant topography. An adaptive version of the proposed scattering-based PPB filter for denoising of SAR images including both mountainous and flat areas is also developed. The performances of the proposed filter and its adaptive version are evaluated both qualitatively and quantitatively in numerical experiments using both simulated and actual SAR images. The proposed technique exhibits performance superior w.r.t. the standard PPB filter and comparable or, in some cases, superior to the state of the art, both in terms of speckle reduction and texture and detail preservation.

In this paper, we present a new framework for high-level processing of time series images, with particular reference to Sentinel-1 data. The proposed methodology has the goal of enhancing the interpretation of SAR imagery through the production of physical-based RGB composites, which are particularly suited for being easily interpreted by the human photo-interpreter, lowering the expertise level required for managing SAR data.

In this paper we propose a methodology devoted to exploit high resolution radars for monitoring water bodies in semi-arid countries. The proposed approach is based on appropriate registration, calibration and processing of SAR data, producing information ready to use by end-users. The obtained results were used to (i) estimate a relationship between surface and volume of water stored in reservoirs and (ii) validate a hydrological model that simulates the time evolution of water availability.

In this paper we describe preliminary results obtained in the framework of a project selected in the context of the joint Announcement of Opportunity for the synergic use of Cosmo-SkyMed and RADARSAT-2 data. The project involves the analysis of SAR images of urban areas and, in particular, the synergic use of Cosmo/SkyMed and RADARSAT-2 data.

Speckle reduction in Synthetic Aperture Radar (SAR) images is an essential pre-processing step for a correct analysis and interpretation of SAR data. This justifies the huge effort in the image processing community to develop more and more accurate despeckling techniques in order to reduce speckle effects and then improve readability of SAR imagery also for non SAR expert users. Up to now, nonlocal means approaches provide the most promising and effective despeckling performances. In this paper we develop a new non-local means despeckling technique based on electromagnetic scattering mechanisms. The proposed method, based on a physically meaningful similarity criterion for distance evaluation, is theoretically assessed, tested on a simulated SAR image, and compared to the state of the art.

This paper presents the multitemporal adaptive processing (MAP3) framework for the treatment of multitemporal synthetic aperture radar (SAR) images. The framework is organized in three major activities dealing with calibration, adaptability, and representation. The processing chain has been designed looking at the simplicity, i.e., the minimization of the operations needed to obtain the products, and at the algorithms' availability in the literature. Innovation has been provided in the cross-calibration step, which is solved introducing the variable amplitude levels equalization (VALE) method, through which it is possible to establish a common metrics for the measurement of the amplitude levels exhibited by the images of the series. Representation issues are discussed with an application-based approach, supported by examples with regard to semiarid and temperate regions in which amplitude maps and interferometric coherence are combined in an original way.

The retrieval of flooding levels with high-resolution (HR) synthetic aperture radar (SAR) images is presented in this paper. A new framework is proposed. It is based on the inversion of theoretical scattering models initially developed for nonflooded urban areas and here adapted to the flooding case. Starting from the theory, two possible retrieval approaches have been developed and are the main topic of this paper: two possible retrieval approaches have been developed and are the main topic of this paper: the local Single Image Objects Aware (SIObA) and the global Two Image Area Aware (TIArA). These two approaches are conceived to be applicable under different working conditions and consequently holding different properties and reliability. For each of them, a different algorithm is derived and tested, and the retrieval results are validated on a meaningful data set of HR TerraSAR-X images relevant to the Gloucestershire (U.K.) flooding that occurred in year 2007.

A physical approach to synthesize fractal surfaces for a reliable and controllable use within remote-sensing applications is presented in this paper. In particular, the physical criteria to determine the minimum number of tones of the Weierstrass-Mandelbrot (WM) function needed to adequately synthesize realizations of fractional Brownian motion (fBm) processes are analytically derived. The presented rationale relies on considering, in an appropriate range of scales, the WM function as a spectral sampling of the fBm process, and linking the number of sampling functions to the width of the scale range and to the sampling rate; hence, it is shown how to set the lower and the upper scales according to a given remote-sensing problem. In this paper, the condition for determining the WM wavenumber sampling rate is analytically derived. The method is also applied to the efficient simulation of the synthetic aperture radar signal scattered by natural surfaces.

In this paper we present the description of the image-processing issues underlying the estimation of the fractal dimension of natural surfaces from their corresponding synthetic aperture radar images. The choice of the spectral-estimation method is a key issue, due to the special nature of the problem at hand. In particular, fractal spectra present well-known leakage problems; moreover, high-variance issues can arise, due to the limited sample size dictated by the local nature of the problem. Meaningful examples of fractal dimension images are provided and discussed.

In this paper we propose a methodology devoted to exploit high resolution radars for monitoring water bodies in semiarid countries. The proposed approach is based on appropriate registration, calibration and processing of SAR data, producing information ready to use by end-users. The obtained results were used to (i) estimate a relationship between surface and volume of water stored in reservoirs and (ii) validate a hydrological model that simulates the time evolution of water availability.

In this paper we present the Multitemporal Adaptive Processing (MAP3) framework for the definition of a new family of multitemporal, user-oriented products whose information level lays between those of the already available Level 1 and Level 2 products. This framework is organized in three blocks of activities dealing with pre-processing, adaptive processing and representation. Experiments performed on semiarid and temperate datasets testify the reliability of the proposed framework and its independence from the sensor and the scenario.

Due to the finite pulse repetition frequency and non-ideal antenna pattern, the presence of “ghosts” on SAR images of maritime scenes is frequently observed. This phenomenon can lead to an increase in false alarm rate in ship-detection applications. In this paper we propose to use the recently developed “asymmetric mapping and selective filtering” (AM&SF) method for the filtering of azimuth ambiguities on stripmap SAR images as a preliminary step of an adaptive-threshold cell-averaging constant-false-alarm-rate ship-detection algorithm. We show that use of this preliminary filtering step allows us to significantly improve the performance of the ship detection by reducing the false alarm rate, without reducing the detection rate. The proposed framework is positively applied to a couple of Cosmo/SkyMed SAR images.

Data acquired by the last generation of SAR sensors over urban areas, thanks to high resolution, can reveal the presence and main features of buildings. Unfortunately geometric distortions introduced by system geometry and signal multiple reflections make SAR images very complex so we need a scattering model supporting both the visual inspection and the processing for information retrieval. In this paper we propose a scattering model able to describe the SAR response in terms of reflectivity map when a couple of buildings compose the scene. The model is based on Kirchhoff approximation and focuses on multiple reflections phenomenon which produce some linear bright structures in SAR images. A comparison between a real SAR image and a simulatation applying the model is reported.

Fractal dimension is widely recognized as key parameter for the geophysical characterization of natural surfaces. A technique for estimating the fractal dimension of a surface from range cuts of an amplitude SAR image has been recently developed by the authors. In this paper, we explore the potentiality of using fully polarimetric SAR data for the retrieval of the fractal dimension along the azimuth direction. This is obtained exploiting the Polarimetric Two-Scale model for the description of polarimetric data. In particular, specific polarimetric combinations are considered, which provide images with strong dependence on the azimuth slopes of the observed surface. The proposed technique is tested on actual fully polarimetric AirSAR L-band data.

For the new generation of spaceborne SAR sensors a resolution even better than one meter in the spotlight acquisition mode can be obtained. However, due to the involved electromagnetic mechanisms dictating SAR image formation, direct interpretation of very high-resolution (VHR) SAR images is not straightforward. In this paper, we propose the use of the power spectral density (PSD) for the analysis and interpretation of VHR SAR images of urban areas. In particular, we introduce a theoretical model of the PSD of such images, and then we test the validity of the developed model on one COSMO/SkyMed spotlight SAR image of the area of Naples, Italy. Finally, we show how the parameters of the obtained PSD can be related to meaningful physical observables of the imaged urban scene.

In this paper, we first discuss some general problems and issues concerning Shape from Shading (SfS) from Synthetic Aperture Radar (SAR) images and present the state of the art. Starting from this general discussion, we propose a new perspective and introduce a novel approach to SAR SfS based on fractals. In particular, fractal geometry is used to model the shape of natural surfaces, which are here of concern. The scattering mechanisms are described through solutions suitable for fractal surface models; in particular, the Small Perturbation Method (SPM) is used. Considering a simple SAR image model, the forward model is then inverted via an appropriate and extremely low-computational inversion approach and the underlying topography is estimated. The proposed SAR SfS technique is tested and numerically evaluated using an actual SAR image.

We consider a system aimed at improving the resolution of a conventional airborne radar, looking in the forward direction, by forming an end-fire synthetic array along the airplane line of flight. The system is designed to operate even in slant (non-horizontal) flight trajectories, and it allows imaging along the line of flight. By using the array theory, we analyze system geometry and ambiguity problems, and analytically evaluate the achievable resolution and the required pulse repetition frequency. Processing computational burden is also analyzed, and finally some simulation results are provided.

In this paper, the equivalent number of scatterers of a rough scattering surface is defined, physically justified, and evaluated. New-generation spaceborne synthetic aperture radar (SAR) sensors are acquiring images at such a high ground resolution that, quite often, the statistics of these images do not match with those predicted by the classical Rayleigh speckle model. Non-Rayleigh speckle is frequently mathematically modeled via K-distribution in terms of a parameter that presently can be estimated (i.e., a posteriori) on the SAR images and is linked to the number of scatterers per resolution cell. However, to model and predict (i.e., a priori) the statistical behavior of the SAR images, a full characterization of the scatterers is required. To this aim, the concept of equivalent number of scatterers of a rough scattering surface is here defined and physically justified. This parameter is then analytically evaluated in closed form as a function of the roughness of the illuminated surface and of SAR sensor parameters. The presented analytical evaluation applies to both classical and fractal descriptions of the surface roughness. Finally, the dependence of the equivalent number of scatterers on the roughness of the illuminated surface and on SAR sensor parameters is analyzed for a range of values of roughness parameters actually encountered in natural surfaces and by considering typical system parameters of modern high-resolution spaceborne SAR systems. It is shown that, actually, for some combinations of realistic surface and system parameters, the equivalent number of scatterers can be on the order of unity.

Objective performance assessment is a key enabling factor for the development of better and better image processing algorithms. In synthetic aperture radar (SAR) despeckling, however, the lack of speckle-free images precludes the use of reliable full-reference measures, leaving the comparison among competing techniques on shaky bases. In this paper, we propose a new framework for the objective (quantitative) assessment of SAR despeckling techniques, based on simulation of SAR images relevant to canonical scenes. Each image is generated using a complete SAR simulator that includes proper physical models for the sensed surface, the scattering, and the radar operational mode. Therefore, in the limits of the simulation models, the employed simulation procedure generates reliable and meaningful SAR images with controllable parameters. Through simulating multiple SAR images as different instances relevant to the same scene we can therefore obtain, a true multilook full-resolution SAR image, with an arbitrary number of looks, thus generating (by definition) the closest object to a clean reference image. Based on this concept, we build a full performance assessment framework by choosing a suitable set of canonical scenes and corresponding objective measures on the SAR images that consider speckle suppression and feature preservation. We test our framework by studying the performance of a representative set of actual despeckling algorithms; we verify that the quantitative indications given by numerical measures are always fully consistent with the rationale specific of each despeckling technique, strongly agrees with qualitative (expert) visual inspections, and provide insight into SAR despeckling approaches.

High resolution SAR data can be a powerful support mainly in areas where the acquisition of in situ information is hampered by physical or economic obstacles. Purpose of this paper is to present an approach to exploit high resolution SAR data for monitoring the temporal evolution of reservoir characteristics in semi-arid regions. Classical and innovative techniques are tailored on the specific climatic conditions of these regions, characterized by the alternation of a three months wet and a nine months dry seasons. Results from a case study developed in Burkina Faso show that the combined use of amplitude and phase information allows the estimation of the eroded areas and a meaningful monitoring of the reservoirs sedimentation.

In this paper we introduce an algorithm to retrieve the soil moisture content and the surface roughness of a moderately vegetated soil from a fully polarimetric SAR dataset. The estimation procedure is founded on the prediction of the second order statistics of the scattered field provided by the Polarimetric Two-Scale Model combined with a two-component scattering model. The performance of the estimation algorithm is assessed by comparing obtained retrieval results to “in situ” measurements. To this aim, data from Little Washita SMEX'03 campaign available in literature are employed.

In this paper we present first results regarding the analysis of individual business district buildings on very high resolution SAR images. In particular, we focus on the filtering of the periodic patterns appearing in the layover area of the buildings, which are related to façade structure (e.g. floors, windows, balconies). The proposed processing chain is based on spectral analysis of the layover amplitude image, and exploits an adaptive filtering strategy to filter out the periodic components, in order to enhance the non-periodic component related to the presence of different objects on the portion of ground in front of the building, interested by the layover phenomenon. The algorithm is applied on a spotlight high resolution image of the business center of Naples, Italy.

The estimation of the fractal dimension of a natural surface using spectral analysis of amplitude SAR data is a powerful tool for geophysical applications. However, the obtained estimates are influenced by the presence of speckle. In this paper we present first results regarding the analysis of the effects on fractal dimension maps due to the application of despeckling techniques on SAR images. The use of simulated data allows to obtain also multilook images, presenting a very high number of looks, which should represent a virtually speckle-free image. In the results section different despeckling techniques are considered, relaying on both classical spatial filtering and non-local means. The presented results allow to draw meaningful conclusions on the effects of speckle and despeckling on spectral estimation and fractal dimension retrieving.

In this paper the analysis of time series of fractal dimension maps generated from multi-pass SAR images is dealt with. The objective is twofold: on the one hand such an analysis is addressed to assess the performance of the algorithm for the fractal dimension estimation from a single SAR image, i.e., to verify the fractal estimation efficiency on natural scenes within the multiple sensor passes. On the other hand, by exploiting the statistics evaluated starting from the fractal time series, a final fractal dimension map, including only the areas in which the fractal estimation is efficient and strongly denoised from the speckle effect, is obtained. The analysis has been performed on a Cosmo-SkyMed data-set of 42 stripmap images spanning the time period from October 2009 to December 2012, acquired over the Somma-Vesuvius volcanic complex (South Italy), which is in a quiescent stage since the last eruption occurred in 1944.

We present a research project, funded by the Italian Space Agency (ASI), aimed at performing 2D and 3D Focusing of COSMO/SkyMed (CSK) SAR Data. We describe the main objectives of the project, briefly illustrate employed techniques, and finally present the obtained results. The latter show that sub-meter resolution can be achieved in the enhanced spotlight CSK acquisition mode, and that by using 3D focusing it is possible to resolve scatterers at different slant heights within the same range-azimuth resolution cell, even in areas characterized by severe height discontinuities and large thermal dilations effects.

Backscattering from natural rough multilayers is addressed. In order to characterize the radar response patterns of natural rough multilayer, the closed-form solution of the Boundary Perturbation Theory (BPT), specialized to backscattering configuration, is employed in conjunction with a fractal description of the interfacial roughness, involving the fractional Brownian motion (fBm) process. Furthermore, numerical examples are presented and discussed with reference to layered structure of interest for remote sensing scenarios. As a result, BPT-fBm model offers a consistent and plausible explanation of the scattering dependency on the involved parameters, so providing a rationale for understanding the basic scattering properties of natural rough multilayers.

This paper is primarily aimed at formally resolving the seeming discrepancy between the corresponding regimes of validity of two different kinds of perturbative formulations based on boundary perturbation (BPT) and volumetric perturbation (VPRT), respectively. Instead of resorting to purely intuitional considerations, the VPRT regime of validity is obtained by using a formal derivation, which also permits to understand in detail the meaning of the involved approximation. As a result, we demonstrate that these two theoretical constructs addressed to rough multilayer scattering can be now regarded in a conceptually coherent frame. Theoretical analysis is also supported by numerical results.

We propose a comprehensive analysis for the evaluation of the scattering from randomly inhomogeneous semi-infinite media, including both interfacial roughness and volume inhomogeneities, by applying the methodological approach of the Volumetric Perturbative Reciprocal Theory (VPRT). An unifying perspective on the scattering phenomenon considered in its entirety is gained and an analytic scattering model is obtained.

Second-order volumetric perturbative reciprocal theory (VPRT), which has a direct theoretical interest to scattering in random media including up to second-order effects, is presented. Expression for the scattering field are directly given in terms of meaningful reactions, i.e. involving physical observable quantities. Distribution theory for discontinuous test functions provides the rigorous mathematical backbone of the general VPRT formulation, so conferring conceptual elegance to the theoretical construct. In this paper, a conceptual and mathematical new approach of interest in electromagnetic scattering analysis is then introduced and discussed.

Electromagnetic models for surface scattering represent a handy analytical tool useful in many remote sensing applications which exploit the prediction of the scattered field. Of course, the better is the surface model employed to compute the electromagnetic scattering, the more accurate are the forecasts on the scattered field, provided that closed form solutions for the statistics of the field are attainable. Accordingly, it is widely recognized that statistical scale-invariance properties exhibited by natural surfaces within a wide observation scale range are very efficiently described by means of fractal geometry. In this paper we focus on the Kirchhoff solution for the scattering from both classical and fractal surfaces, so allowing us to provide a novel physical interpretation of the Kirchhoff scattering integral in terms of the probability density function of the slopes of an equivalent rough surface.

In this paper the analysis of fractal dimension maps extracted from Cosmo-SkyMed amplitude-only SAR data is considered. The focus is on the Somma-Vesuvius volcanic complex, whose study allows to draw significant comments on the behavior of the fractal maps. In particular, the dependence of the fractal maps on geometrical and geomorphologic characteristics of the imaged surface is investigated. Preliminary results regarding the different behavior of the fractal dimension evaluated on the Gran Cono and on the Mt. Somma are presented.

In this paper we present the results of a project approved in the frame of the 2007 Cosmo-Skymed AO, devoted to use high resolution data for hydrology applications in semi-arid context. A case study was developed in Burkina Faso, a West Africa country, characterized by the alternation of intense rainy and dry seasons. In this paper we present the rationale of the project along with two of the obtained products concerning the estimation of eroded areas and the surface water recharge.

In this paper a SAR simulator able to provide images with the appropriate speckle statistics is introduced. It requires as inputs the radar and surface parameters and it is able to evaluate the number of equivalent scatterers per resolution cell, N. The statistics relevant to each single equivalent scatterer in the resolution cell are effectively generated and the global statistics of the return from the resolution cell are obtained as the coherent sum of the contributions pertaining to each equivalent scatterer. The rationale of the proposed simulation framework is presented, along with significant results regarding the analysis of the simulated images.

In this paper the application of an innovative SAR image processing developed by the authors - that provides a map of the point by point fractal dimension of a single (amplitude) SAR image - to a data-set of COSMO-SkyMed images, with look angles varying from 20° to 45°, is presented. The main objective is the analysis of the fractal behavior of natural and urban scenarios, with particular evidence to the fact that fractal dimension estimation, in the case of SAR images of natural areas, turns out to be independent of the sensor viewing geometry, while for urban areas it is strongly dependent on look angle variations.

We propose a new framework for the quantitative assessment of SAR despeckling techniques, based on physical-level simulation of SAR images corresponding to canonical scenes. Thanks to the simulator, we can generate multiple SAR images of the same scene which differ only in the speckle content, and, hence, a true multilook SAR image, with an arbitrarily large number of looks, to use as “speckle-free” reference. Based on this concept, we select a small set of canonical scenes and, for each of them, a suitable set of objective measures which account for speckle suppression and image feature preservation. We gain insight into the system reliability by comparing the indications it gives for some sample despeckling filters with those obtained by expert visual inspection of the filtered images.

An algorithm able to retrieve the soil moisture content and the surface roughness of a bare soil from a set of measured HH-, VV-SAR images is here presented. The estimation procedure is founded on the prediction of power densities and correlation between the copolar channels provided by the Polarimetric Two-Scale Model that we recently proposed. The performance of the estimation algorithm is assessed by comparing obtained retrieval results to “in situ” measurements. To this aim, data from Little Washita SMEX'03 campaign available in literature are employed.

This paper presents the main scientific novel outcomes of the research activities included in the project “Buildings Feature Extraction from Single SAR Images: Application to COSMO-SkyMed High Resolution SAR Images”. The project was conceived by the authors of this paper and financed by the Italian Space Agency under the Announcement of Opportunity for the COSMO/SkyMed mission.

In this paper we present a procedure for the calibration of SAR data devoted to extract physical information from Cosmo-Skymed images. The main purpose is the extraction of land cover indexes for hydrology applications. The calibration procedure was implemented in a rural context in Burkina Faso, where along with the technical challenges we faced socio-economic problems. A set of handmade trihedral corner reflectors were deployed and their response measured from SAR images. The results were used for calibration of an erosion product.

In this paper we present a physical approach for the synthesis of fractional Brownian motion (fBm) with the use of Weierstrass-Mandelbrot functions (WM). The quantitative equivalence between WM and fBm is still an open problem, and it is here faced by showing that a WM can be seen as a spectral sampling of an fBm process. The presented physical framework provides the quantitative definition of the sampling frequency as a function of the physical properties of the fractal surface. The obtained results can be employed for the evaluation of the electromagnetic field scattered from natural surfaces, which is a topic of broad interest for the remote sensing community. The comparison domain and the results for the sampling frequency parameter will change, in agreement with the physical mechanisms that govern the scattered field formation.

The Italian Space Agency (ASI Agenzia Spaziale Italiana) funded 27 scientific projects in the framework of COSMO-SkyMed (CSK) program. A subset of them focused on the improvements of the quality and quantity of information which can be extracted from X-SAR data if integrated with other independent techniques (GPS, data and imagery in other bands and wavelengths). The paper summarizes the results obtained from same of these projects and, in particular, regarding: the use of GPS observations and Numerical Weather Models (NWM) to remove atmospheric artifacts from InSAR imagery so improving the CSK potentialities in the field of topographic mapping; the integration of SAR data in X, L and C bands to improve snow cover monitoring and glaciers detection; the use of X-SAR data to retrieve rain precipitation and its validation with C-band radar observations; the improvements of the focusing techniques.

A complete theoretical model for synthetic aperture radar (SAR) imaging of natural surfaces is introduced in this paper. The topography of the natural scenes is described via models derived from fractal geometry; scattering evaluations are performed via fractal scattering models appropriate to the employed fractal scene description. Scattering contributions are combined according to the SAR image impulse response function. The power spectral density of appropriate cuts of the SAR image are evaluated in closed form in terms of the surface fractal parameters. Our theoretical model is here conceptually assessed, analytically derived, graphically validated, numerically verified, and also tested on simulated SAR images. The introduced model allows defining innovative postprocessing inverse techniques to retrieve fractal parameters directly from SAR images.

We propose a polarimetric two-scale surface scattering model employed to retrieve the surface parameters of bare soils from polarimetric synthetic aperture radar or scatterometer data. The scattering surface considered here is composed of slightly rough randomly tilted facets, for which the small perturbation scattering method holds. The facet random tilt causes both a random variation of the local incidence angle and a random rotation of the local incidence plane around the line of sight. Unlike other similar already existing approaches, our method considers both these stochastic effects in the analytical evaluation of the facet scattering matrix, and their statistical modeling is derived from a proper statistical description of the scattering surface. In particular, we assume that the facet slope (i.e., the slope of the large-scale surface roughness) is a Gaussian random variable, in agreement with both classical and fractal surface models. The proposed scattering model is then used to retrieve bare soil moisture and (large-scale) roughness from the co- and cross-polarized ratios. The performance of the resulting retrieval algorithm and its validity limits are finally assessed by comparing obtained results to “ in situ” measurements. To this aim, data from different measurement campaigns available in literature are employed.

A new concept for change detection algorithm for urban areas affected by earthquake is here presented. It is characterized as applicable to just one post-event amplitude Synthetic Aperture Radars (SAR) image and employs the inversion of sound scattering models already introduced in literature by the same authors. Aim of the algorithm is to try obtaining fast mapping of damaged areas and provide a first, even rough, evaluation of damage reported. In particular in this paper the overall block diagram chain and the algorithm rationale behind that framework are introduced and discussed in details. Some preliminary results are presented and the performance analyzed. New possible applications based on similar rationale are also commented.

We present a theoretical model for the evaluation of electromagnetic transmission through a gently rough wall. The pertinent closed-form solution is obtained in the theoretical framework of the boundary perturbation theory (BPT). Specifically, for a slab with rough interfaces we derive a compact polarimetric solution directly expressed in terms of the (Fresnel) ordinary reflection coefficient only. Numerical results are carried out with reference to the canonical structure by considering two-dimensional Gaussian rough interfaces, and relevant scattering patterns are presented.

A polarimetric two-scale surface scattering model employed to retrieve the surface parameters of bare soils from polarimetric SAR data is proposed. The scattering surface is considered as composed of slightly rough randomly tilted facets, for which the Small Perturbation Method holds. The facet random tilt causes a random variation of the local incidence angle, and a random rotation of the local incidence plane around the line of sight, which in turn causes a random rotation of the facet scattering matrix. Unlike other similar already existing approaches, our method considers both these effects. The proposed scattering model is then used to retrieve bare soil moisture and (large-scale) roughness from the co-polarized and cross-polarized ratios. The performances of the resulting retrieval algorithm is finally assessed by comparing obtained results to “in situ” measurements. To this aim, data from Little Washita campaign available in literature is employed.

In this paper we present a physical model for the description of the speckle in Synthetic Aperture Radar (SAR) images. The proposed model highlights the analytical dependence of the speckle characteristics on the surface roughness and the sensor parameters. The illuminated surface is represented using the fractal geometry. In addition, a SAR raw signal simulator able to generate SAR images whose speckle characteristics are coherent with the observed surface properties is presented.

In this paper an innovative fractal based filtering for the analysis of SAR images of natural surfaces is presented. Its definition is based on a complete direct imaging model developed by the authors. The application of this innovative algorithm to SAR images makes it possible to obtain a complete map of the fractal dimension of the observed scene. Significant results obtained on actual SAR data are shown in the last section of the paper.

There is no doubt that retrieving observed scene features is one of the most interesting and challenging activities in all fields of remote sensing: The successful extraction of scene parameters may not only mean the success of the adopted procedure but also the success of a prediction model, an image product, a sensor project, or even an entire mission. This paper is partly concerned with this. The mission, the sensor, and the products at issue are the TerraSAR-X; the feature retrieval approach is the deterministic model-based approach already tested on E-SAR images and now in phase of improvement and testing on high-resolution TerraSAR-X images. Together with assessing the performances of TerraSAR-X products, this paper deals with a new application which, until now, has not received enough attention even if being worth of it: monitoring of big tanks in suburban or urban areas. Detailed discussion concerning the most suitable product for this kind of application is accompanied by retrieval results carried out on recently acquired TerraSAR-X images.

Detection of man-made structures in urban areas, in terms of both geometric and electromagnetic features, from a single, possibly high resolution (HR), synthetic aperture radar (SAR) image is a highly interesting open challenge. Within this framework, a possible approach for the extraction of some relevant parameters, describing the shape and materials of a generic building, is proposed here. The approach is based on sound electromagnetic models for the radar returns of each element of the urban scene. A fully analytical representation of electromagnetic returns from the scene constituents to an active microwave sensor is employed. Some possible applications of feature extractions from real SAR images, based on the aforementioned approach, have already been presented in the literature as first examples of potentiality of a model-based approach, but here, the overall theory is analyzed and discussed in depth, to move to general considerations about its soundness and applicability, and the efficiency of further applications may be derived. For the sake of conciseness, although the proposed approach is general and can be applied for the retrieval of different scene parameters (in principle, anyone contributing to the radar return), we focus here on the extraction of the building height, and we assume that the other parameters are eithera prioriknown (e.g., electromagnetic properties of the materials) or have been previously retrieved from the same SAR image (e.g., building length and width). An analysis of the sensitiveness of the height retrieval to both model inaccuracies and errors on the knowledge of the other parameters is performed. Some simulation examples accompany and validate the solution scheme that we propose.

In this paper, a model for radar images of fractal (topologically 1-D) profiles is introduced. A twofold approach is followed: on one hand, we analytically solve the problem whenever small-slope profiles are in order; on the other hand, we present a partly analytical and partly numerical setup to cope with the general-slope case. By means of the analytical approach, we evaluate in closed form both the structure function and the power density spectrum of the radar signal. An appropriately smoothed (physical) fractional Brownian model (fBm) process is employed; its introduction is justified by the finite sensor resolution. A fractal scattering model is employed. It is shown that for a fractal profile modeled as an fBm stochastic process, the backscattered signal turns out to be strictly related to the associated fractional Gaussian noise process if a small-slope regime for the observed profile can be assumed. In the analytical–numerical framework, a profile with prescribed fractal parameters is first synthesized; then, fractal scattering methods (applicable to wider slope regimes with respect to the previous case) are employed to compute the signal backscattered toward the sensor. Finally, the power density spectrum of the acquired radar image is estimated. The obtained spectra are favorably compared with the theoretical results, and a parametric study is performed to assess the overall method behavior.

Fractal geometry is widely accepted as an efficient theory for the characterization of natural surfaces; the opportunity of describing irregularity of natural surfaces in terms of few fractal parameters makes its use in direct and inverse electromagnetic (EM) scattering theories highly desirable. In this paper, we present an innovative procedure for manufacturing fractal surfaces and for measuring their scattering properties. A cardboard-aluminum fractal surface was built as a representation of a Weiestrass-Mandelbrot fractal process; the EM field scattered from it was measured in an anechoic chamber. A monostatic radarlike configuration was employed. Measurement results were compared to Kirchhoff approximation and small perturbation method closed-form results that were analytically obtained by employing the fractional Brownian motion to model the surface shape. Matching and discrepancies between theories and measurements are then discussed. Finally, fractal and classical surface models are compared as far as their use in the EM scattering is concerned.

Cylindrical-shape tanks are typical of any suburban area and often contain dangerous gases or fluids. In this paper, we suggest a way to monitor them by means of high resolution Synthetic Aperture Radar (SAR) images and a scattering model able to quantitatively consider how the radar signal interacts with this kind of structures and how they appear m the SAR images. Adopting the model, geometrical information as the tank height is retrieved from the SAR images m a non-conventional way that is exploiting the information content contained m the double reflection contribution to the radar cross section. Results are compared with more traditional methods and discussed.

A closed-form first-order perturbative solution to the problem of electromagnetic scattering from a layered structure with an arbitrary number of rough interfaces is presented in this paper. Following the classical scheme employed to deal with a rough surface, a perturbative expansion of the fields in the rough-interface layered structure is performed, assuming that roughness heights and slopes are small enough. In this manner, in the first-order approximation, the geometric randomness of the corrugated interfaces is translated into random current sheets imposed on the unperturbed (flat) interfaces and radiating in the unperturbed (flat boundaries) layered media. The scattered field is then represented as the sum of up- and down-going waves, and a systematic approach that involves the use of matrix formalism and generalized reflection/transmission coefficients is employed. This approach permits us to avoid the necessity of the cumbersome Green function formalism. The demonstration of the consistency of the presented solution is analytically provided, showing that the proposed solution reduces to the corresponding existing ones when the stratification geometry reduces to the simplified ones considered by the other authors.

This paper presents a new systematic simulation procedure to compute the altimeter signal return, thus showing the correlation of the signal return shape with height, slope, sub-surface layers and different scales of roughness of the surface of interest. For the first time, and as appropriate to natural surfaces, the scene is fully modelled by means of the fractal geometry. By observing the results presented in this paper, it appears that an advanced processing of altimeter data is possible.

Analysis, interpretation and feature extraction concerning High Resolution (HR) Synthetic Aperture Radar (SAR) images of urban areas urgently require support of sound and appropriate electromagnetic modelling. The modelling takes into consideration the radar geometry and the (geometric and electromagnetic) scene parameters but also the novelty brought by high resolution. In this paper, this way of developing suitable electromagnetic modelling for HR SAR images of urban areas is shown to be successful as able to interpret and retrieve, from these scenarios, new and interesting details that will certainly represent the main actor of next generation of applications for urban areas with SAR sensors.

In this paper, we present an elegant closed form solution obtained applying perturbation theory to three-dimensional layered structures with an arbitrary number of rough interfaces. A general method has been developed to treat layered structures that can be described by small changes with respect to an idealized (unperturbed) structure, whose associated problem is exactly solvable. The final expression, beyond the compactness, allows us to directly prove that our formulation satisfies the reciprocity principle.

In this paper the focus is on the intrinsic significance of the first-order perturbative scattering solution for the scattering from layered structures with an arbitrary number of rough interfaces. An exact analytic decomposition of the solution in terms of local interactions is carried out. Consequently, the suitably expanded solution can be expressed as an optical geometric series, where each term of the series is susceptible of a direct physical interpretation. Therefore, the obtained interpretation enables the global scattering phenomenon involved to be visualized as a superposition of local interactions, emphasizing the role of the interference effects in the structure as well. The interpretation opens the way to new techniques for solving the inverse problem, for designing SAR processing algorithms, and for modelling the time-domain response of complex layered structures.

In this paper a first step toward a complete model of the fractal imaging process is taken: for the sake of simplicity the mathematical details are here provided for a fractal profile with topological dimension equal to one. In particular, we show how the signal backscattered from a fractal profile modeled as a fractional Brownian motion (fBm) stochastic process is strictly linked to an associated fractional Gaussian noise (fGn) process. We compute in closed form the power density spectrum of the received signal in the simplified hypothesis of a linear dependence of the backscattered signal on the profile derivative process. Our results apply to physical fBm processes, as dictated by the low-pass filtering introduced by both the incident electromagnetic field wavelength and the finite sensor resolution. In the last section a numerical study of the above mentioned is also provided.

In this paper, we investigate analytically the connection between the existing first-order small perturbation method solutions for the scattering from a layered structure with one rough interface. First of all, by using effectively the concept of generalized reflection coefficients, we cast the existing models in a unified more compact formulation and point out the connection between the different analytical solutions. The obtained reformulations of the available analytical solutions allow us to subsequently prove the consistency of the considered models. Finally, a suitable expansion is performed that leads us to understand the physical meaning of the analytical expressions. The obtained unified formulation also opens the way toward a general closed-form solution for the problem of scattering by a layered structure with an arbitrary number of corrugated interfaces.

The identification of oil slicks on the ocean surface from SAR data requires quantitative sound models accounting for the most important characteristics (ocean spectrum, slick viscosity, slick shape, and so on). In this paper we present the implementation of an innovative SAR raw signal and image simulator, which is able to reproduce images relative to ocean surfaces covered by oil slicks with arbitrary shapes. The attention is mainly focused on slicks with fractal contours. The fractal Weierstrass-Mandelbrot function is used to generate slicks with fixed fractal dimension. A box counting technique is employed to evaluate the fractal dimensions of the generated slicks and the corresponding SAR images. Radiometric properties of the area covered by oil are also estimated in order to show how the simulated data provide a powerful set for processing algorithms.

Interpretation of high resolution SAR (synthetic aperture radar) images is still a hard task, especially when man-made objects crowd the scene under detection. This paper contributes to the analysis of this kind of data by adopting an approach, based on a scattering model, for the retrieval of buildings height from real SAR images and presenting first numerical results.

In this paper we introduce an original formulation for the electromagnetic field diffraction by a knife edge with random roughness: the formulation, based on the Asymptotic Physical Optics (APO) approach, leads to closed form evaluations of the statistics of the diffracted field. A very interesting conclusion is that, for moderate edge roughness, the diffracted field propagation can be described in terms of the same ray congruence as in the straight (smooth) edge case, with the only difference that the field associated to each ray is a random variable whose statistics are, in this paper, computed in closed form. The presented approach can be then considered as a first step toward a general Stochastic Theory of Edge Diffraction (STED).

The fractal geometry proved to be the most appropriate mathematical instrument in describing natural scenes, by means of few effective and reliable geophysical parameters. In this paper we use fractal concepts to model and to identify geometrical changes occurred in areas hit by disasters. We present an overall framework employing fractal based models, algorithms and tools to support identification of natural area changes due to natural or man-made disasters. The proposed framework includes a SAR raw signal simulator, which is of key importance to improve the comprehension of the mechanisms underlying SAR image formation. In addition, we consider, as a case study, the simulation and detection of lava flows in a volcanic scenario. The potentialities of our technique for the discrimination between different types of lava are presented and discussed.

Thanks to their capability to cover large areas, in all weather conditions, during the day as well as during the night, spaceborne Synthetic Aperture Radar (SAR) techniques constitute an extremely promising alternative to traditional surveillance methods. Nevertheless, in order to assure further usability of SAR images, specific data mining tools are still to be developed to provide an efficient automatic interpretation of SAR data. The aim of this paper is to introduce texture analysis performed in the framework of time - frequency theory, as a means to detect oil spills in the sea surface. In particular, an algorithm permitting a precise quantitative characterization of the border between the oil spill candidate and the sea, will allow a novel classification of oil spills and look-alikes.

In this paper, we present a complete framework to support the monitoring of natural and man-made disasters by means of synthetic aperture radar (SAR) images. The fractal geometry is the most appropriate mathematical instrument in describing the irregularity of a natural observed scene, by means of few effective and reliable parameters. Therefore, fractal concepts can be used to model and identify geometrical changes that occurred in areas hit by disasters. We present an overall framework employing fractal-based models, algorithms, and tools to support the identification of natural area changes due to natural or man-made disasters. Such a framework includes an algorithm used to extract fractal parameters from a 2-D signal, a fractal interpolation tool, and a SAR raw-signal simulator. The combined use of these tools provides an innovative instrument for disaster monitoring applications. In this paper, we implement the fractal framework to obtain a relation between the fractal parameters of a SAR image and those of the relative imaged area. In addition, a case study is discussed, showing the potentiality of our framework for flooding detection

Fractal behavior of one Titan profile acquired by the Cassini altimeter during its first flyby is demonstrated and quantitatively analyzed. The inadequacy of popular nonfractal models to represent scale-dependent behavior is also discussed. Our results lead to the conjecture that a proper geometrical model of the Titan surface is provided by the fractional Brownian motion stochastic process and that the particular acquired profile has a fractal dimension of about 2.4. In addition, use of the developed statistical model allows the design of model-based filters for a significant improvement of the measurement accuracy, at the expense of the horizontal resolution

Synthetic aperture radar (SAR) raw signal simulation is a useful tool for SAR system design, mission planning, processing algorithm testing, and inversion algorithm design. A two-dimensional (2-D) Fourier domain SAR raw signal simulator, exploiting the efficiency of fast Fourier transform algorithms, has been presented some years ago and is able to generate the raw signal corresponding to extended scenes. However, it cannot account for the effects of sensor trajectory deviations with respect to the nominal straight-line path. This paper explores the possibility of extending the efficient Fourier domain simulation approach to the case of sensor trajectory deviations, which is more realistic for airborne SAR systems. We first of all obtain a general and compact Fourier domain formulation of the SAR raw signal in the presence of arbitrary trajectory deviations, and show that in this general case no efficient simulation scheme can be devised. However, we demonstrate that, if a narrow beam and slow trajectory deviation assumption is made, a full 2-D Fourier domain simulation can be used. This approach can be applied only to some SAR systems and/or trajectory deviations, but it has the advantage that processing time is practically not increased with respect to the nominal trajectory case. The validity limits of the approach are analytically evaluated. Some simulation results are finally presented in order to verify the effectiveness of the proposed simulation scheme. In another paper, which is the second part of this work, it will be shown that the narrow beam-slow deviation assumption can be relaxed, at the expense of computation efficiency, if a one-dimensional azimuth Fourier domain processing followed by a range time-domain integration is used

In a previous paper, a two-dimensional Fourier domain synthetic aperture radar (SAR) raw signal simulator that exploits the efficiency of fast Fourier transform algorithms was presented. It accounts for the effects of sensor trajectory deviations and is able to generate the raw signal corresponding to extended scenes in a few seconds. However, a narrow-beam-slow-deviation assumption is made; hence, the approach can be applied only to some SAR systems and/or trajectory deviations. To overcome this limitation, in this paper, we show that the narrow-beam-slow-deviation assumption can be relaxed, at the expense of computation efficiency, if use is made of one-dimensional azimuth Fourier domain processing followed by range time-domain integration. The latter approach only requires some reasonable assumptions on the sensor motion and on the SAR system features; hence, it can be used for airborne SAR systems, and turns out to be still much more efficient than the time-domain one; hence, extended scenes can still be considered. Validity limits of the approach are also analytically evaluated, and several simulation results are finally presented to verify the effectiveness of the proposed simulation scheme

Fractal geometry provides reliable models to describe geometrical properties of natural surfaces. Therefore, their use in the electromagnetic scattering methods deserves careful research. In order to have complete insight into the phenomenon, a measurement campaign on a fractal surface in a controlled environment is a key step. In this paper, we propose a technique for building a fractal surface that can be used for electromagnetic scattering evaluation purposes. The surface characteristics are imposed by computer synthesizing a bandlimited Weierstrass-Mandelbrot function, whose actual shape is constructed by means of a cheap innovative technique: the synthesized surface is made from cardboard covered with aluminum foil, which gives a conducting surface and creates the micro-scale conditions, useful to represent manufacturing errors. Statistics of the overall surface shape are then measured, analyzed and compared with the imposed ones, providing and verifying the rationale for a fully controlled surface to be applied in any kind of experiment on natural surfaces.

An extended scene SAR raw signal simulator accounting for sensor trajectory deviation is here presented. A general Fourier Domain formulation of the SAR raw signal in presence of arbitrary trajectory deviations is obtained, and it is shown that, under the assumption that the radar beam is sufficiently small and the trajectory deviations are sufficiently slow, a very efficient full 2-D Fourier domain simulation can be used. © 2005 IEEE.

We propose a soil dielectric constant retrieval scheme based on the use of the ratio of power densities scattered at HH and VV polarizations along the specular direction for different incidence angles and/or frequencies. The method relies on the minimum squares technique and is based on the observation that at variance with the backscattering case, in the specular case the small perturbation method (SPM) and the Kirchhoff approach both lead to the same expression of the copolarized ratio. Accordingly, it is expected that its evaluation is rather robust, to hold under a wide range of surface roughnesses. We present method-of-moments (MoM) simulations that confirm this expectation, and we test the validity of the overall retrieval scheme, as well as its sensitivity to measurement errors (calibration errors and fading or speckle noise), by applying the algorithm to data simulated by using the MoM. Theoretical and practical problems related to the implementation of measurements along the specular direction are also discussed. The proposed method is amenable to the new generation of bistatic remote sensing instruments.

The extended boundary condition method with the Weierstrass-Mandelbrot fractal function (WM-EBCM) has been recently employed to model and solve the problem of electromagnetic scattering from natural surfaces. In this paper we first of all show, on the basis of theoretical considerations and of numerical examples, that this method can be used also for the evaluation of electromagnetic emission from natural surfaces. In addition, a small roughness approximation of the WM-EBCM solution is presented to highlight the connection between EBCM and SPM, and to avoid matrix ill-conditioning in scattering problems. Achieved results show that the zero-order scattered field is the (deterministic) field reflected by the mean plane, and that the first-order (random) scattered field is directly proportional to surface roughness. Validity limits of the approximated method are discussed and verified by studying the scattered field behavior at different surface roughness conditions.

Recently, a new operating mode for Synthetic Aperture Radar (SAR) system, referred to as hybrid stripmap/spotlight mode, has been presented. In the hybrid acquisition mode the radar antenna beam is steered about a point farther away from the radar than the area being illuminated, thus generating microwave images with an azimuth resolution better than that achieved in the stripmap configuration, and a ground coverage better than the one of the spotlight configuration. The subject of design, processing and data interpretation for the hybrid SAR mode is gaining an increasing interest in the remote sensing scientific community. Consequently, a hybrid SAR raw signal simulator is strongly required, especially when real raw data are not available yet, to test processing algorithms and to help mission planning. In addition, to analyse the effects of processing errors and to verify the impact of different system design choices on the final image for different kinds of imaged scenes, an extended scene SAR raw signal simulator is very useful: it is what we present in This work. After showing that in this case a 2D Fourier domain approach is not viable, we demonstrate that a 1D range Fourier domain approach, followed by 1D azimuth time domain integration, is possible when some approximations, usually valid in the actual cases, are accepted.

In this paper we analytically evaluate the correlation coefficient between the signals backscattered towards two spatially separated antennas by a building modelled as a parallelepiped over a possibly rough terrain. We consider both single and multiple scattering contributions.

In this paper we present a procedure devoted to validate innovative theories on the electromagnetic scattering from natural surfaces, using fractal functions for the surface description. A fractal surface has been built as a superposition of cardboard and aluminium layers, according with a computer design based on the Weierstrass-Mandelbrot (WM) fractal function. Its statistical properties have been verified with optical measurements. The electromagnetic field scattered at X band from the built surface has been measured in controlled environment as a function of the incidence angle. Obtained results have been compared with those of the Kirchhoff Approximation (KA) and the Small Perturbation Method (SPM) in conjunction with the fractional Brownian motion (fBm). Congruence and discrepancies of measured data with theoretical results are discussed, providing a validation of the methods

The hybrid stripmap/spotlight mode for a synthetic aperture radar (SAR) system is able to generate microwave images with an azimuth resolution better than the one achieved in the stripmap mode and a ground coverage better than the one of the spotlight mode. In this paper, time- and frequency-domain-based procedures to simulate the raw signal in the hybrid stripmap/spotlight mode are presented and compared. We show that a two-dimensional Fourier domain approach, although highly desirable for its efficiency, is not viable. Accordingly, we propose a one-dimensional (1-D) range Fourier domain approach, followed by 1-D azimuth time-domain integration. This method is much more efficient than the time-domain one, so that extended scenes can be considered. In addition, it involves approximations usually acceptable in actual cases. Effectiveness of the simulation scheme is assessed by using numerical examples.

In this paper we propose a soil dielectric constant retrieval scheme based on the use of the ratio of power densities scattered at hh and vv polarisations along the specular direction for different incidence angles and/or frequencies. The method relies on the minimum squares technique, and is based on the observation that, at variance with the backscattering case, in the specular case the small perturbation method (SPM) and the Kirchhoff approach (KA), both under the scalar approximation (SA) and under the stationary phase approximation (SPA), lead to the same expression of the copolarised ratio, that should hold under a wide range of surface roughness. We present method of moments (MoM) simulations that confirm this expectation, and we test the validity of the overall retrieval scheme.

A physics-based electromagnetic model specially tailored for the computation of the radar backscatter from rice crops is being developed. The model produces estimates of the coherent polarimetric backscatter response as a function of the sensor parameters and the physical description of the scene. This model is currently under validation against experimental data gathered in the European Microwave Signature Laboratory (EMSL). First results, obtained by a direct comparison of simulated and measured backscattering coefficients (with HH, HV and VV polarizations), show a reasonable agreement. The dependence on incidence angle, frequency and morphological characteristics of the rice plants is currently being assessed.

In this paper we show that existing small perturbation-based methods for computation of scattering from a layered structure with a single rough interface are substantially equivalent. In addition, a further reformulation of the scattered field expression allows us to express the final solution in series form: we then show that each term of the series has a direct physical interpretation.

In this paper, the field scattered by a 2-D Weierstrass-Mandelbrot (WM) perfectly conducting fractal surface is evaluated, by using the Extended Boundary Condition Method (EBCM). Integral equations are set in the frame of the extinction theorem. The surface fields are then evaluated by exploiting the WM property of being an almost periodic function. In principle, no assumption on the surface roughness is made. Each component of the scattered field is expressed as a superposition of generalised Floquet modes, whose directions of propagation follow the grating equation and whose amplitudes are expressed in terms of matrixes with no finite dimension. How to truncate the matrixes sets the validity limits. The reliability of the method is tested by means of an energy criterion.

Synthetic aperture radar (SAR) raw signal simulation is a powerful tool for designing new sensors, testing processing algorithms, planning missions, and devising inversion algorithms. In this paper, a spotlight SAR raw signal simulator for distributed targets is presented. The proposed procedure is based on a Fourier domain analysis: a proper analytical reformulation of the spotlight SAR raw signal expression is presented. It is shown that this reformulation allows us to design a very efficient simulation scheme that employs fast Fourier transform codes. Accordingly, the computational load is dramatically reduced with respect to a time-domain simulation and this, for the first time, makes spotlight simulation of extended scenes feasible.

A synthetic aperture radar (SAR) raw signal simulator for urban scenes is presented along with models it employs to compute the backscattered field and to account for the imaging radar sensor characteristics. Urban areas are represented as a set of dielectric buildings placed over a random rough nonflat dielectric terrain. The adopted models allow evaluation of the raw signal on a sound physical and mathematical background: the scattering model employs the Kirchhoff approach appropriately extended to include multiple-reflection effects, and the radar model operates in the two-dimensional Fourier transformed domain. Details to assess how and why the simulator is also efficient with respect to the computational time are provided. In addition, relevant examples are discussed to show the simulator potentialities and assess the validity of the main results. It is shown that the simulator is able to quantitatively assess performances of SAR sensors over urban structures. The proposed simulator turns out to be also useful to train numerical schemes devoted to feature extraction, and to test any specific SAR processing technique. Comments for further developments of the simulation tool are presented.

In this paper a spotlight SAR raw signal simulator for extended scenes is presented. The proposed procedure has two main advantages: first of all, use is made of efficient FFT codes, thus reducing the computational load with respect to a time-domain simulation; in addition, the procedure is planned taking into account theoretical developments used in an existing stripmap simulator, so that most of the algorithms employed in that simulator can be reused after minor changes.

Electromagnetic modeling and its use in SAR raw signal simulation constitute powerful instruments for the interpretation of SAR images. As far as the urban environment is concerned, the need of such instruments is higher, for the complexity of the mechanisms that contributes to the formation of the scattered field. We present an analytical model for the scattering from typical elements of the urban environment. The obtained closed form solution is then employed in a SAR raw signal simulator. Simulated images well match with the expected characteristics.

In this paper, a geometric and electromagnetic model of a typical element of urban structure is presented, in order to analytically evaluate in closed form its electromagnetic return to an active microwave sensor. This model can be used to understand what information on geometric and dielectric properties of a building can be extracted from microwave remote sensing data. The geometrical model consists of a rectangular parallelepiped whose vertical walls form a generic angle with respect to the sensor line of flight. The parallelepiped is placed on a rough surface. The radar return from such a structure can be decomposed into single-scattering contributions from the (rough) ground, the building roof (a plane surface in our model), and vertical walls and multiple scattering contributions from dihedral structures formed by vertical walls and ground. In our model, single-scattering contributions are evaluated by using either physical optics (PO) or geometrical optics (GO), depending on surface roughness. In order to account for multiple scattering between buildings and terrain, we use GO to evaluate the field reflected by the smooth wall toward the ground (first bounce) or the sensor (second or third bounce) and GO or PO (according to ground surface roughness) to evaluate the field scattered by the ground toward the wall (first or second bounce) or the sensor (second bounce). Finally, the above model is used to analyze the field backscattered from a building as a function of the main scene parameters; in particular, the angle between vertical walls and sensor line of night and the dependence on the look angle are analyzed.

In this paper, we employ the extended boundary condition method with the Weierstrass-Mandelbrot (WM) fractal function to model and solve a relevant electromagnetic scattering problem. The key point of the procedure is the property of the WM to be an almost periodic function. This allows to generalize techniques employed for periodic problems and to express the field by means of a superposition of Floquet modes. The procedure is devised for the general case of dielectric surfaces. Criteria for assessing the validity of the method are discussed and provided. Validity of the method is confirmed by numerical results.

Synthetic aperture radar (SAR) raw signal simulation is a powerful tool for design of oil slick detection and interpretation systems. In this paper, the ocean simulation issues are presented, and the main problems relating to the oil presence on the sea surface are treated. Attention is focused on the electromagnetic side of the problem, taking account of the sensor signatures, the dielectric, physical-chemical, and geometric nature of the oil slick, and the environmental conditions. The presented SAR simulator is based on an ocean model and an oil slick model. The former makes use of multiscale description of the ocean surface: the distributed surface model for the SAR-ocean interaction is considered by taking into account the nonlinear hydrodynamic effect for the water particle movement. The latter model implements a modification of the ocean spectrum, based on the Marangoni theory and accounting for the nonlinear wave interaction mechanism. However, the proposed SAR raw signal simulator is modular and flexible, thus allowing other possible physical models for modeling the oil slick effect over the ocean spectrum. Meaningful SAR simulation experiments are presented and discussed, elucidating the role of difference on pollutants, oil thickness, wind speed and direction, incident wavelength and angle and other radar parameters. Validation of the simulator is also presented by comparison with experimental data. A striking conclusion of the paper is that higher order moments (from the second on) of oil slick SAR image statistics are quite different compared to those pertinent to an equivalent wind speed decrease on the imaged area. This suggests a convenient way to define new appropriate oil slick signatures.

A new theoretical framework for retrieval of dielectric and geometric surface parameters from electromagnetic backscattering data is presented. Use is made of fractal geometry for description of the surface. The suggested scheme leads to a two-step procedure that allows the authors to retrieve the surface complex permittivity and the fractal parameters. Limits of validity for the procedure applicability are presented. The robustness of the method is checked against input data measurement errors and electromagnetic model inaccuracies. Key points for application of the new framework to synthetic aperture radar (SAR) data are discussed.

A prelaunch study on performances of the X-SAR/SRTM mission in case of outboard antenna mast bending is presented. The paper provides the assessment of SAR products, raw data complex images, and interferograms. Theoretical analysis shows the influence of mast oscillation parameters on each SAR product. Employing a SAR raw data simulator, a numerical study on canonical as well as actual scenes is also presented. Numerical results confirm theoretical analysis and quantitatively show SAR products degradation.

This paper presents the rationale for the use of method of moments (MoM) for evaluation of electromagnetic field scattered by dielectric random fractal surfaces. Derivation of the algorithm is given along with a reliable fractal profile generation procedure. Numerical results are compared and validated against those obtained under the hypotheses of the small perturbation scattering theory, available together with the applicability limits in the current literature. Choice of key parameters pertinent to MoM is also discussed. Numerical results are finally used to investigate the behavior of the electromagnetic scattering from fractal surfaces.

A novel across-track interferometric synthetic aperture radar (SAR) raw signal simulator is presented. It is based on an electromagnetic backscattering model of the scene and an accurate description of the SAR system impulse response function. A set of meaningful examples are also presented. They show that the proposed simulator is structurally consistent and correctly simulates the decorrelation effect, both in the mean and in the distribution sense.

Simulation of synthetic aperture radar (SAR) raw signal pertaining to an ocean scene is presented. It embodies the distributed surface model, the hydrodynamic modulation, and the Bragg dominant scattering mechanism. Simulation experiments are provided.

The authors exploit the interferometric multifrequency potentiality of the SIR-C/X-SAR system which is equipped with an L-, C-, and X-band sensor. They present a solution to improve the unwrapping performance of the C- and X-band data by considering the L-band unwrapped pattern. A new algorithm for the generation of a single digital elevation model (DEM) combining L-, C-, and X-band information is presented. This solution is based on the fusion of the unwrapped phase patterns by using a Kalman filter. The proposed fusion operation also accounts for the coherence characteristics of the three data sets. The selected test site is the Mt. Etna region in Italy which is very interesting from the volcanological and geological point of view. Numerical assessments of the achieved results are provided by evaluating the height accuracy with respect to a reference DEM.

Deals with the simulation of synthetic aperture radar (SAR) raw signal of actual ground sites described in terms of sparse input data. Since in most cases the input data reference system does not match SAR requirements, it is necessary to adopt appropriate interpolation schemes. The authors focus their attention on elevation input data, noting that natural surfaces exhibit fractal properties. Fractal and nonfractal interpolation schemes are discussed and applied. Simulated images are shown and compared to actual examples. Subjective and objective tests validate the simulation and support the fractal-based elevation interpolation.>

An SAR simulator of an extended three-dimensional scene is presented. It is based on a facet model for the scene, asymptotic evaluation of SAR unit response, and a two-dimensional fast Fourier transform code for the data processing. Prescribed statistics of the model account for a realistic speckle of the image. The simulator is implemented in Synthetic Aperture Radar Advance Simulators (SARAS), whose performance is described and illustrated by a number of examples.>