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.

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.

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.

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.

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.

Sentinel-2 data represent a rich source of information for the community due to the free access and to the temporal-spatial coverage assured. However, some of the spectral bands are sensed at reduced resolution due to a compromise between technological limitations and Copernicus program's objectives. For this reason in this work we present a new super-resolution method based on Convolutional Neural Networks (CNNs) to rise the resolution of the short wave infra-red (SWIR) band from 20 to 10 meters, that is the highest resolution provided. This is accomplished by fusing the target band with the finer-resolution ones. The proposed solution compares favourably against several alternative methods according to different quality indexes. In addition we have also tested the use of the super-resolved band from an applicative perspective by detecting water basins through the Modified Normalized Difference Water Index (MNDWI).

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 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.

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.

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.

We propose a new synthetic aperture radar (SAR) despeckling technique based on nonlocal filtering and driven by a coregistered optical image. A preliminary homogeneous versus heterogeneous classification of the image is used to decide where the optical guide can be safely used, thus preventing any distortion of the SAR geometry. Even in regions where the use of optical data is enabled, despeckling is carried out exclusively in the SAR domain, and the optical guide is used only to improve the predictor selection in nonlocal filtering and, hence, in the estimation process. Experiments on real-world imagery confirm the potential of the proposed approach.

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.

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.

Reliable segmentation of SAR images requires some forms of user supervision: we resort here to the interactive version of the Tree-Structured Markov Random Field (TS-MRF) segmentation suite. The TS-MRF model, and the associated segmentation tool, provide a flexible and spatially adaptive description of the data. In the interactive version, the user can drive the process based on the inspection of the current result, deciding step-by-step which direction to take, and switching from one segmentation modality to another. Experiments with the segmentation and classification of multitemporal SAR images prove the potential of the interactive approach and of the TS-MRF tool.

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 address the problem of SAR despeckling by resorting to nonlocal filtering guided by an optical image. In fact, given the increasing availability of remote-sensing optical images, it makes perfect sense trying to use them to improve the performance of despeckling. Our technique exploits the optical image to reliably estimate the statistical similarity among pixels, which is used to evaluate the weights of nonlocal filtering. Optical data are not used to estimate SAR values, but only to guide the overall process. In addition, they are discarded altogether in regions where SAR and optical images present different local geometries, identified by a preliminary classification step, avoiding thus any additional distortion. Experimental results show the proposed approach to provide images of better quality than state-of-the-art conventional filters.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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, 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.