Large-scale synthetic environments are essential for advancing smart cities research. However, existing urban benchmarks typically prioritize either visual photorealism or geometric fidelity, often neglecting the material-level semantics and physical attributes required for simulations. To address this gap, this paper introduces UR-MAT , a modular design framework and a multimodal synthetic dataset specifically built to bridge computer vision and physics-based modelling. We present a unified construction workflow that integrates georeferenced geometry, physically based rendering (PBR) materials, and domain-specific metadata (e.g., electromagnetic properties based on ITU-R standards) into a traceable data representation. The resulting dataset encompasses eight heterogeneous urban scenarios, providing spatially aligned RGB imagery, depth maps, semantic segmentation masks, and 3D point clouds. The utility of UR-MAT is validated through cross-domain experiments: (i) a quantitative baseline analysis on semantic material segmentation across fourteen material classes, where the best-performing model achieved 0.787 mIoU and 0.968 pixel accuracy, and (ii) electromagnetic ray-tracing simulations, which verify that the dataset’s material annotations yield physically consistent and deterministic radio propagation patterns. By enabling perception and simulation tasks within a single coherent framework, UR-MAT serves as a resource for developing perception-driven communication strategies and next-generation Digital Twins.

The development of smart city applications increasingly relies on datasets that capture not only urban geometry but also the material properties that influence real-world interactions. However, most existing datasets lack detailed semantic labels and physical attributes, limiting their applicability to tasks such as signal propagation modeling and material-aware analysis. In this paper, we propose a structured process and a tool pipeline for generating material-aware synthetic datasets from custom 3D urban scenes. Our approach integrates 3D modeling, physically-based rendering, and automated data capture to produce multimodal outputs, including RGB images, depth maps, segmentation masks, structured metadata, and annotated 3D meshes. We demonstrate the process and pipeline instantiation using the Louvre Museum district as a running scenario, capturing the site's complex architecture and material diversity. A preliminary ray-tracing simulation in MATLAB further demonstrates how the generated data can support material-aware applications. By bridging geometry, appearance, and physical information, the proposed solution can contribute to advancing simulation-ready datasets for smart city research.

This study investigates the subsidence phenomenon in the Capo Colonna area (Calabria, southern Italy) using Synthetic Aperture Radar (SAR) data from C and X bands, and Continuous Global Positioning System (CGPS) measurements collected between 2003 and 2018. Persistent Scatterer Pair Differential Interferometry Synthetic Aperture Radar (PSP-DIFSAR) and Small Baseline Subset (SBAS) techniques were employed to produce detailed deformation maps. With ascending and descending datasets from various satellites, along with acquisition parameters such as incidence and heading angles, the Line of Sight (LOS) measurements were decomposed into vertical and east-west displacement components. This provided a comprehensive understanding of the displacement rates in the Capo Colonna peninsula, revealing significant subsidence patterns, particularly in the promontory. Varied deformation rates were observed across different sectors, with higher rates in the peninsula than in Crotone's hinterland and urban areas. The complementary comparison between SAR and CGPS data offered critical insights into the region's geodynamic behavior, enhancing risk assessment and management strategies to preserve its geological and archaeological heritage.

Hybrid compact polarimetric (CP) SAR systems present significant advantages with respect to fully polarimetric (FP) ones, in terms of hardware simplification and better attainable resolution and coverage. Retrieval of soil moisture is a classical application of FP SAR data: in this context, advanced model-based techniques have been proposed to support microwave data inversion. In this paper, we present a new approach for soil moisture retrieval from CP data, based on the use of the Polarimetric Two-Scale Model (PTSM), which has been originally proposed in support of soil moisture retrieval from FP data. Obtained results are validated using simulated CP data relevant to the AgriSAR campaign test sites and compared with FP PTSM-based retrieval results.

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.

Natural surfaces exhibit scale-invariance properties and power-law spectra over a wide range of scales. For this reason, they are well described in the frame of fractal geometry and, in particular, using fractional Brownian motion (fBm) two-dimensional statistical processes. In this paper, we present the derivation of the correlation of Global Navigation Satellite System reflectometry signals scattered by a fBm surface. We here show that this correlation depends on a parameter related to the root-mean-square surface slope as measured at the electromagnetic wavelength scale. When this parameter increases, the correlation smoothly decreases from a value close to unity reaching the value of the roughness-independent expression already available in the literature. In our experiments, based on roughness measurements available in the literature, we firstly show how the description of the roughness of natural surfaces can be conveniently obtained via fBm parameters. Then, we illustrate the behavior of the correlation time for different roughness regimes. For a high-altitude airborne receiver, values ranging from about 6 ms (as predicted by the expression already available in the literature) to several tens of milliseconds are obtained.

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.

This research examines the application of Interferometric Synthetic Aperture Radar (InSAR) from space for tracking and evaluating surface and structural changes in transport networks, particularly a 65 km stretch of the A16 highway in Southern Italy. The project merges various elements like the design planimetry of the infrastructure, SAR image datasets, measurements taken on-site, and an updated Landslide Inventory Map (LIM) to validate the compatibility of SAR outcomes with conventional monitoring techniques, especially inclinometers. Considering the widespread presence of erosional and gravitational events, such as landslides in this area, effective surveillance is crucial. The investigation utilizes COSMO-SkyMed imagery from 2015-2022. These data are processed via the Coherence Pixels Technique (CPT) algorithm, delivering numerous Persistent Scatterers (PSs). Inclinometers were used to record ground deformations and authenticate DInSAR products, showing alignment between the two different methodologies. The research highlights the value of DInSAR measurements for overseeing infrastructural changes caused by geological occurrences like landslides. These data could be integrated into an early warning mechanism, supporting decision-making processes for managing transport networks.

Sea and soil surfaces exhibit power-law spectra over a wide range of spatial frequencies. An analytical formulation of the electromagnetic scattering from such surfaces can be obtained via the two-scale model (TSM). However, this approach requires the definition of a cutoff surface wavenumber, separating the low- and high-frequency parts of the surface spectrum. The final obtained normalized radar cross section (NRCS) value is dependent on the choice of this cutoff wavenumber, which is, to some extent, arbitrary. This problem can be avoided by describing power-law spectrum surfaces via the theory of fractional Brownian motion (fBm) two-dimensional (2-D) random processes. The bistatic NRCS of an fBm surface can be analytically evaluated by using the Kirchhoff approximation (KA) or the first-order small slope approximation (SSA-1): its expression is related to the probability density function (pdf) of an alpha-stable random process, and it can be efficiently evaluated by means of proper asymptotic series expansions. However, fBm surfaces are statistically isotropic, whereas natural surfaces are often anisotropic. Therefore, in this work, we first of all show that an anisotropic power-law spectrum surface can be considered as a generalized anisotropic fBm surface; then, we present an analytical formulation of its NRCS, based on SSA-1; and finally, we compare the obtained results with measured NRCSs of natural surfaces and with NRCS values obtained via more accurate but more computationally demanding methods that require the numerical evaluation of scattering integrals.

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.

We present an analytical formulation of the correlation of the electromagnetic fields scattered by a rough or gently undulating surface and measured at two spatially separated points. The latter may be the positions of two different bistatic radar sensors receiving the scattered field at the same time, as in bistatic synthetic aperture radar interferometry, or two positions occupied by a single moving receiver at two slightly different times, as in Global Navigation Satellite System Reflectometry. The scattering surface is modeled as randomly rough, and the Kirchhoff Approximation is employed to compute the scattered field. The obtained closed-form expression of the field correlation is substantially coincident with the one already available in literature for far-from-specular scattering directions; conversely, for close-to-specular directions, the obtained formulation shows that, as the surface correlation length increases, the degree of coherence smoothly increases from the value obtained with the expression available in literature for rough surfaces to a value close to unity for very gently undulating surfaces.

We present an analytical formulation of the correlation coefficient of the electromagnetic fields scattered at near-specular direction by a rough or gently undulating surface and measured at two spatially separated positions occupied by a moving receiver at slightly different times. This allows us obtaining an explicit expression of the correlation time of the received signal in terms of radar and surface parameters. This work provides a contribution to the discussion, currently ongoing in the Global Navigation Satellite System Reflectometry (GNSS-R) scientific community, about the behavior of received signal fluctuations, especially when surface profile variations are such that the scattering is neither coherent nor completely incoherent. The scattering surface is here modeled as randomly rough, and the Kirchhoff Approximation (KA) or the first-order Small Slope Approximation (SSA1) is employed to compute the scattered field. In fact, the expression of the correlation coefficient is the same for both approximations. The obtained closed-form expression shows that, as the surface correlation length increases, the degree of coherence smoothly increases from the value obtained with the expression already available in literature for very rough surfaces to a value close to unity for gently undulating surfaces. The obtained behavior of correlation time as a function of surface parameters, system resolution and observation geometry is in agreement with numerical simulations available in literature. More in general, obtained analytical results are in agreement with the observed behavior of GNSS-R signals over flat land surfaces.

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.

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.

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.

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.

Ship detection is a fundamental task for SAR-based maritime surveillance. Besides providing high reliability, a good detector is required to be computationally light, in order to analyze huge areas in a reasonable time. We propose a fully convolutional neural network for ship detection in SAR images. Thanks to a relatively simple architecture, complexity remains low enough to allow for a single-stage approach, thus avoiding the possible errors of CFAR pre-screening. Experiments on a Sentinel-1 dataset prove the proposed CNN to be much more reliable than CFAR detection.

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.

Recently, we have proposed a new synthetic aperture radar (SAR) technique, which we called “Coprime SAR” (CopSAR), applicable to the case of bright targets over a dark background and therefore useful in ocean monitoring for ship detection. The CopSAR technique is based on the adaptation of the coprime array beamforming concept to the case of SAR systems, and, in its basic implementation, it is able to reduce the amount of data to be stored and processed, with no geometric resolution loss. A more complex dual-frequency implementation allows us to additionally obtain a significant increase in the range swath size with respect to the standard SAR technique. However, for both practical and theoretical reasons, the simplest single-frequency implementation is preferable. Accordingly, here, we present an enhancement of the CopSAR basic implementation, based on the transmission of (quasi) orthogonal waveforms, i.e., up- and down-chirps: we name it “Orthogonal Coprime SAR” (OrthoCopSAR). The proposed implementation is able to achieve both data reduction and range swath extension with no appearance of ghosts, no resolution loss, and only a limited complication of the required technology. The only costs are the reduction in the target-to-background ratio and the presence of a (nonstringent) limit on maximum ship size, as it is the case in all CopSAR implementations.

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 present a new technique able to reduce the amount of data to be stored and processed in order to obtain large swath SAR image in marine scenarios. The proposed technique is an enhancement of the recently proposed CopSAR technique and it is based on the transmission of (quasi) orthogonal waveforms, i.e. up- and down-chirps. The proposed implementation is able to achieve both data reduction and range swath extension with no appearance of ghosts, no resolution loss, and with only a limited complication of the required technology. The only costs are the reduction of the target-to-background ratio and the presence of a (non-stringent) limit on maximum ship size.

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.

In this paper, we propose a synthetic aperture radar (SAR) technique able, in the case of bright targets over a dark background, to reduce the amount of data to be stored and processed, and, at the same time, to increase the range swath, with no geometric resolution loss. Accordingly, the proposed approach can be usefully employed in ocean monitoring for ship detection. The technique consists of a new SAR acquisition mode and of very simple processing. It is based on the adaptation of the coprime array beamforming concept to the case of SAR systems: two interlaced sequences of pulses are transmitted, with two sub-Nyquist pulse repetition frequencies (PRFs) that are equal to the Nyquist PRF divided by two coprime integer numbers. Each sequence is separately processed via standard SAR processing, and the two final aliased images are combined in a very simple way to cancel aliasing. We call the proposed approach “coprime SAR” (CopSAR). Three different implementations are proposed, and the effectiveness of the CopSAR concept is demonstrated by using both simulated and real SAR data. It turns out that the significant data amount reduction and the range swath increase are only paid with a reduction of the target-to-background ratio and with the presence of a (nonstringent) limit on ship azimuth size.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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