Gerardo Di Martino

Benchmarking Framework for SAR Despeckling

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.

Filtering of Azimuth Ambiguity in Stripmap Synthetic Aperture Radar Images

Due to the specific characteristics of the SAR system, peculiar artifacts can appear on SAR images. In particular, finite pulse repetition frequency (PRF) and nonideal antenna pattern give rise to azimuth ambiguity, with the possible presence of “ghosts” on the image. They are due to the replica of strong targets located outside of the antenna main beam, superposed onto low intensity areas of the imaged scene. In this paper, we propose a method for the filtering of azimuth ambiguities on stripmap SAR images, that we name “asymmetric mapping and selective filtering” (AM&SF) method. Our framework is based on the theory of selective filtering and on a two-step procedure. In the first step, two asymmetric filters are used to suppress ambiguities due to each sidelobe of the antenna pattern, and the ratios between the original and filtered images are used to produce two maps of the ambiguity-affected areas (one for each sidelobe). In the second step, these maps are used to produce a final image in which only the areas affected by the ambiguities are replaced by their filtered (via the proper of the two filters) versions. The proposed method can be employed in situations in which similar approaches fail, and it has a smaller computational burden. The framework is positively tested on TerraSAR-X and COSMO/SkyMed SAR images of different marine scenes.

Effectiveness of high-resolution SAR for water resource management in low-income semi-arid countries

This article presents an efficient framework and a sustainable pilot project on the effective use of spaceborne synthetic aperture radar (SAR) in low-income countries and semi-arid climatic contexts. The technical efficiency was pursued by integrating SAR models and hydrological assessment methods; the socio-economical sustainability was guaranteed by the joint work of scientists, technicians, and volunteers. The pilot project was developed in the Yatenga region, a Sahelian area in northern Burkina Faso. In particular, an original development of SAR interferometry algorithms was tailored to the peculiar climate, the soil characteristics, and the land cover of the semi-arid regions. A digital elevation model (DEM) was derived, and an original approach based on the use of SAR amplitude images is proposed for its validation. The achieved resolution (9 m) is significantly better than that of the previously available DEMs in the study area (30 m). Based on the DEM, the soil sedimentation rate of small reservoirs was estimated together with the average soil loss in the contributing catchments due to the erosion process. A multi-temporal filter was implemented on the SAR images for monitoring of water intake volume in small reservoirs, and its seasonal evolution. The developed tools provide an innovative contribution for the improvement of water resource management in the study area. This approach is repeatable and scalable to suit situations with similar economic and climatic conditions.

Sentinel-1 for Monitoring Reservoirs: A Performance Analysis

In this paper we explore the performances and the opportunities provided by the European satellite Sentinel-1 for water resource management applications in low-income countries. The analysis is supported by a synthetic aperture radar (SAR) simulator, which allowed the quantification of the expected characteristics of Sentinel-1 products in three applications: interferometric digital elevation models (DEMs) generation, land cover mapping and estimation of water volumes retained by small reservoirs. The obtained results quantitatively show that Sentinel-1 data characteristics are fully suitable for most of the application already explored in the recent SAR literature.

Scattering-Based Nonlocal Means SAR Despeckling

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.

Imaging of Fractal Profiles

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.

Coprime Synthetic Aperture Radar (CopSAR): A New Acquisition Mode for Maritime Surveillance

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.

Modeling Watershed Response in Semiarid Regions With High-Resolution Synthetic Aperture Radars

In this paper, we propose a methodology devoted to exploit the outstanding characteristics of COSMO-SkyMed for monitoring water bodies in semiarid countries at a scale never experienced before. The proposed approach, based on appropriate registration, calibration, and processing of synthetic aperture radar (SAR) data, allows outperforming the previously available methods for monitoring small reservoirs, mainly carried out with optical data, and severely limited by the presence of cloud coverage, which is a frequent condition in wet season. A tool has been developed for computing the water volumes retained in small reservoirs based on SAR-derived digital elevation model. These data have been used to derive a relationship between storage volumes and surface areas that can be used when bathymetric information is unavailable. Due to the lack of direct measures of rivers discharge, the time evolution of water volumes retained at reservoirs has been used to validate a simple rainfall-runoff hydrological model that can provide useful recommendation for the management of small reservoirs. Operational scenarios concerning the improvement in the efficiency of reservoirs management and the estimation of their impact on downstream area point out the applicative outcomes of the proposed method.

Scattering-Based SARBM3D

Interpreting synthetic aperture radar (SAR) images may be a very challenging task, even for expert users. One of the main reasons is the multiplicative speckle noise typical of coherent acquisition systems. Therefore, despeckling can be expected to play a key role in the full exploitation of SAR imagery potential. However, even state-of-the-art despeckling algorithms neglect the physical phenomena hidden behind SAR imagery. Image acquisition depends on electromagnetic scattering, which is also at the basis of speckle noise. Taking into account scattering issues into more physical-based despeckling algorithms may only benefit the overall performance. In this paper, we propose a scattering-based (SB) version of the SAR block-matching 3D (BM3D) filter, named SB-SARBM3D. SARBM3D can be arguably considered as one of the most promising and accurate despeckling algorithms, providing a good compromise between speckle reduction and detail preservation. We modify the original algorithm so as to exploit the prior information available on the imaged scene, taken into account based on scattering concepts. The new algorithm is tested in a variety of different and complementary simulated scenarios, and its performance is assessed objectively by means of numerous synthetic parameters. Moreover, comparison with different state-of-the-art despeckling algorithms is performed on some actual SAR images, both inherent to natural and urbanized areas, for subjective evaluation. Thanks to the prior information, SB-SARBM3D outperforms the original algorithm in terms of both speckle reduction and detail preservation. Moreover, it reduces the annoying artifacts introduced sometimes by SARBM3D in homogeneous areas of the image.

Urban Areas Enhancement in Multitemporal SAR RGB Images Using Adaptive Coherence Window and Texture Information

In this paper, we present a technique for improving the representation of built-up features in model-based multitemporal synthetic aperture radar (SAR) RGB composites. The proposed technique exploits the multitemporal adaptive processing (MAP3) framework to generate an a priori information which is used to implement an adaptive selection of the coherence window size. Image texture is used to support the coherence information in case of decorrelation. The coherence information, powered by texture analysis, and combined with backscattering amplitude, provides a unique representation of built-up features. This allows for an immediate detection of urban agglomerates by human operators, and is an advantaged starting point for urban area extraction algorithms.