In the framework of developments of 3D forest Tomography, the issue of extensive and detailed characterizations of temporal decorrelation phenomena has recently emerged, especially for the future spaceborne missions. In particular, height-varying (stratified) behaviour of long-term temporal decorrelation mechanisms has been analyzed by advanced 4D (3D+Time) Differential SAR Tomography processing applied to airborne data, and dedicated radar-tower campaigns have been conducted or are running. In this work, new Differential Tomography analyses exploiting a very quick acquisition ground-based miniradar are presented, aiming to open investigation of both height- and time-varying characteristics of the short-term decorrelation processes of the complex moving and non-stationary (dynamic) volumetric scatterers of windblown forests. This innovative characterization methodology and the first reported findings can be useful especially for the development of advanced spaceborne Tomography systems based on formation-flying and 3D correlative track-pair only processing.

The classification of high-resolution and fully polarimetric SAR/ISAR data has gained a lot of attention in remote sensing and surveillance problems and is addressed by decomposing the radar target Sinclair matrix. In this paper, the Sinclair matrix has been projected onto the circular polarization basis and is decomposed into five parameters that are invariant to the relative phase$\Phi$, the Faraday rotation$\Omega$, and the target orientation$\Psi$without any information loss. The physical interpretation of these parameters, useful for target classification studies, is found in the wave-particle nature of radar scattering phenomenon given the circular polarization of elemental packets of energy. The proposed deterministic target decomposition is based on the left-orthogonal special unitary SU(2) basis, decomposing the signal backscattered by point targets, represented by the target vector, via six special unitary SU(4) rotation matrices, and by providing full resolution and lossless analysis. Comparisons between the proposed deterministic target decomposition and the Cameron, Kennaugh, Krogager, and Touzi decompositions are also pointed out. Generally, the proposed decomposition provides simpler interpretation, faster parameter extraction, and better generalization properties for the analysis of nonreciprocal or random targets. Several polarimetric SAR/ISAR data sets of UWB data, airborne fully polarimetric EMISAR data, and spaceborne RADARSAT2 are used for illustrating the effectiveness and the usefulness of this decomposition for the classification of point targets. Results are very promising for application use in the next generation of high-resolution spaceborne and airborne Pol-SAR and Pol-ISAR systems.

As recently demonstrated, passive radars are able to detect and track targets by exploiting illuminators of opportunity. In this paper, it will be proven that the same concept can be extended to passive inverse synthetic aperture radar (P-ISAR) imaging. A suitable type of signal processing is proposed that is able to form P-ISAR images starting from range-Doppler maps, which represent the output of passive-radar signal processing. Multiple-channel digital television broadcasting (DVB)-T signals are used to demonstrate the concept as they provide enough range resolution to form meaningful ISAR images. The problem of grating lobes, which are generated by the DVB-T signal, is also addressed and solved.

Advanced satellite SAR systems, such as Cosmo SkyMed, provide high resolution images with reasonably short revisiting time, allowing for a number of applications in areas such as Homeland Security and Maritime Surveillance. Especially in the case of maritime surveillance, non-ccoperative moving targets imaging represent a challenge for SAR systems as the complex target motions produce evident image defocusing. In the present paper, we show evidence of the effectiveness of the applications of ISAR processing to obtain well focused images of such type of targets. It will be also shown that super-resolution techniques are applicable to targets affected by complex motions after ISAR processing is used.

In the paper are presented some of the main outcomes of the projects, relevant to marine monitoring, developed in the framework of the first Announcement of Opportunity Cosmo-SkyMed. The principal subjects covered are: ship detection, oil spill monitoring, wind retrieval, sea surface currents estimation and coastline extraction.

In this paper, the authors propose an innovative technique which is applied to the sea Synthetic Aperture Radar (SAR) imagery in order to aid the detection of the oil tankers responsible for oil spills. This technique aims to identify these oil spills and to correlate them to the detected ships. Real data acquired from COSMO-SkyMed (CSK) system have been processed to verify the effectiveness of this proposed new algorithm.

The target coherency or covariance matrices are the main operators useful for characterizing the polarization transformation properties of radar target by modeling the depolarization effect. In this paper, a novel decomposition of the target coherency matrix is proposed, that is sufficient for representing the physical characteristics of the observed medium in term of a minimum set of orientation invariant parameters. The Einstein's photon circular polarization basis is used for obtaining an orientation invariant physical interpretation of the proposed parameter set both for deterministic and random target. A generalized unsupervised classification scheme is also proposed for underlining the effectiveness of the proposed decomposition theorem for classifying random reciprocal target into 75 physically meaningful clusters. The application of the proposed decomposition theorem and classification algorithm is useful for developing of novel Remote Sensing products and Data Mining softwares for monitoring the surfaces of the Earth and the Moon.

In this paper, the problem of classifying nonhomogeneous man-made targets is investigated by performing a macroscopic and detailed target analysis. The Cloude-Pottier H/ αMLdecomposition is used as a starting point in order to find orientation-invariant feature vectors that are able to represent the average polarimetric structure of complex targets. A novel supervised classification scheme based on nearest neighbor decision rule is then designed, which makes use of the feature space. A validation process is performed by analyzing experimental data of simple targets collected in an anechoic chamber and airborne EMISAR images of eight ships. Three classification robustness performance indicators have been evaluated for each feature vector by performing the leaves-one-out-method described by Mitchell and Westerkamp. The robustness of the classifier has been tested with respect to the ability to reject unknown targets and to correctly identify known targets.

Passive radar systems exploit non-cooperative transmitter to detect targets in areas of interest. Some of the main advantages of such systems with respect to conventional radars include low cost architectures, low energy requirements and potentially null probability of intercept. In this paper a low-cost solution for vehicles detection making use of passive radar concept is presented. A Software Defined Radio (SDR) solution and commercial antennas have been used to realize a DVB-T passive radar demonstrator. An analysis of the DVB-T signal is firstly presented together with a study of its capability as radar waveform. Afterwards an experimental setup is presented and analysed and finally some results of targets detection are shown.

Sea Normalized RCS, and Doppler spectra have been revised for HF-OTH Clutter Modelling. The Hasselmann model is firstly introduced to predict the sea directional spectrum of fetch-limited sea and results have been compared with the Pierson-Moskovitz model used for large scale ocean remote sensing. Results show that the closed fetch-limited sea has lower NRCS compared with ocean for similar wind intensity and direction. For this reasons RCS and Doppler spectra must be predicted taking into account of the fetch dimension. In future work we will generalize this interesting approach to fetch-limited wind, time-limited pulse, in order to show the waveform effect on Doppler spectrum.

One of the main benefits brought by the use of fully polarimetric radars is the ability to identify scattering mechanisms, which are related to the target physical properties. One of the most critical problems in synthetic range-profile reconstruction is the distortion induced by the target motion. Radial target velocity and acceleration generate second- and third-order phase terms in the received signal, which produce range shift and point-spread-function smearing. The distortions induced by the target motion produce, as a consequence, a signal-to-noise ratio loss. Recently, a method based on contrast maximization has been proposed in order to compensate target radial motions using single-polarization data. In this paper, an extension of such an algorithm is proposed that exploits fully polarimetric data in order to improve the target radial motion compensation.

Inverse synthetic aperture radar (ISAR) images are often used for classifying and recognizing targets. Moreover, the use of fully polarimetric ISAR (Pol-ISAR) images enhances classification capabilities. In this paper, the authors propose a novel automatic target recognition (ATR) technique based on the use of fully Pol-ISAR images and neural networks (NNs). In order to reduce the amount of data processed by the classifier, the brightest scattering centers are first extracted by means of the Pol-CLEAN technique, and then, their scattering matrices are decomposed using Cameron's decomposition. A classifier based on the use of multilayer perceptron NN that makes use of the features extracted from the Pol-ISAR images is then implemented. A proof-of-concept test is performed on real data acquired during a controlled experiment in an anechoic chamber.

Inverse Synthetic Aperture Radar (ISAR) images are often used for classifying and recognising targets. Moreover the use of a fully polarimentric ISAR image enhances classiication capabilities. In this paper, the authors propose a novel ATR technique based on the use of fully polarimetric ISAR images and Neural Networks. In order to reduce the amount of data processed by the classifier, the brightest scattering centres are first extracted by means of the Pol-CLEAN technique and then their scattering matrices are decomposed using Cameron's decomposition. The proposed ATR algorithm is finally tested on real data.

Scattering type classification represents a significant step toward target classification. Both the surface of Poincare's sphere and Cameron's unit disc have been used separately to represent symmetric scattering matrices and to define classification methods. In this letter, the equivalence of using the surface of Poincare's sphere and Cameron's unit disc in terms of characterization and classification of symmetric scattering types is demonstrated mathematically.

In stepped-frequency radar, target motions produce range-profile distortions. Range shift, signal-to-noise ratio loss, and symmetric spreading are produced by target radial velocity, whereas target radial acceleration is mainly responsible for asymmetric smearing. Acceleration-distortion effects are usually negligible when a high Pulse Repetition Frequency (PRF) is used, although this is not the case for low-PRF radars. In this paper, a new motion-compensation technique based on contrast optimization is proposed. The innovative contributions of this paper are as follows: (1) A theoretical analysis of the distortions produced by target motions on the reconstruction of synthetic aperture radar is provided; (2) the proposed technique compensates both phase terms, which are due to target radial velocity and acceleration; therefore, synthetic range profiles can be focused by processing low-PRF radar returns; (3) a new cost function for the synthetic range profiles (namely, contrast) is defined and used for motion compensation; (4) the proposed technique can be applied to any kind of stepped-frequency waveforms; and (5) an estimation error analysis is performed, first theoretically and then by means of both simulations and real data.

In stepped frequency radar, target motions produce range profile distortion. Specifically, the target radial velocity causes range profile shift, point spread function symmetric spreading and peak reduction, whereas the target radial acceleration is responsible for both asymmetric and symmetric point spread function spreading. This paper proposes a contrast- based technique for estimating the target motion parameters and therefore for reducing range profile distortions.

Cameron's coherent target decomposition and classification is able to represent a symmetric scatterer onto a unit disc in the complex plane, and assign it to one of the six symmetrical elemental scatterer classes. Recently, Touzi et al. proposed a variation of Cameron's method by introducing a coherent analysis. Moreover the Poincare's sphere, was used instead of the unit disc for representing symmetric scattering because it was considered a more suitable domain. The aim of this work is to demonstrate the equivalence of using Poincare's sphere domain and Cameron's unit disc, in term of characterisation and classification of symmetric scattering types.

Inverse synthetic aperture radar (ISAR) images are frequently used in target classification and recognition applications. Some classifiers often require features that can be more easily obtained by extracting scattering centers from ISAR data rather than by reconstructing ISAR images. An available method for scattering center extraction, namely, the CLEAN technique, was proposed in a recent paper by Yanget al. In this paper, an improvement of this CLEAN technique is proposed that introduces a new method for detecting scattering centers. The proposed technique is based on a Gaussianity test, and its effectiveness is first theoretically proven and then tested on real data. Moreover, a comparison with the technique proposed by Yanget al. is shown.

In ISAR systems, fully polarimetric capabilities have not been fully exploited for target classification or recognition. In this paper, a full system that reconstructs the polarimetric ISAR image and classifies the target is proposed and tested on simulated data.

This paper proposes a novel algorithm for estimating the fractal dimension of sea synthetic aperture radar (SAR) images. The algorithm is based on the variation method, and it is suitably designed for the analysis of sea SAR images. The SAR image fractal dimension is a feature that provides a measure of the image roughness. Such a feature can play an important role in the classification process for recognizing the presence of anomalies on the sea surface. The innovation aspects of this paper are listed as follows: (1) an extension of the variation method, which was proposed for the fractal analysis of one-dimensional signals, to the case of two-dimensional (2-D) functions; (2) a numerical formulation of the variation method, which is suitable for processing 2-D discrete signals; and (3) an optimization of the algorithm for sea SAR image analysis. The algorithm is tested and validated both on simulated and real ERS-1/2 Precision Image sea SAR images and compared with the classical estimation algorithm based on spectral analysis

In hybrid SAR/ISAR systems full knowledge of the radar-target geometry is often not available. In such cases, both the cross-range scaling factor and the image plane are not known a-priori and they have to be estimated in order to classify the target. The modulus of the effective rotation vector can be estimated from the data and cross-range scaling can be achieved. Nevertheless, the orientation of such a vector, which is orthogonal to the image plane, can only be estimated in particular cases and from a long sequence of images. In this paper a novel technique, based on the use of bistatic radar imaging systems, for estimating the total rotation vector is proposed.

In this paper we consider the phase degradation performance between an ideal radar path and that of a sea surface reflected signal (over a variety of sea surface heights) in an Emulated Bistatic Radar scenario. The sea surface model employed is a ID Fractal model that is based on the Generalised Weierstrass Function. Graphical and Tabular results are provided that demonstrate the absolute performance using the simulation technique discussed, for sea heights up to 0.9m.

This paper introduces a new analysis technique, using the fractionally integrated autoregressive-moving average (FARIMA) model, to distinguish between low-wind and oil slick areas in high-resolution sea synthetic aperture radar (SAR) imagery. The method deals with the estimation of the fractional differencing and autoregressive-moving average parameters of the mean radial power spectral density of sea SAR images. The algorithm is applied and validated on dark areas corresponding to oil slicks, oil spills, and low-wind sea surface anomalies in European Remote Sensing 1 and 2 Precision Images of the Mediterranean Sea, North Sea, and Atlantic Ocean.

Over many years of research, several ISAR autofocusing techniques have been proposed. Today, we can divide them into two main categories: parametric and non-parametric techniques. The prominent point processing and the phase gradient algorithm are two classical examples of non-parametric techniques, whereas the more recent image contrast and entropy based techniques represent a new generation of parametric techniques. In this paper, the advantages and disadvantages of each technique are highlighted and a performance analysis is carried out by means of ISAR image reconstruction of real data.

SAR images from space-borne platforms have proved to be helpful data for identification of oil spills and other surface anomalies, such as low wind areas, man-made targets, and natural films. The use of fractal dimension, which is related to the concept of surface "roughness", as a feature for classification, improves the detection of anomalies, since enhances texture discrimination. In the particular case of oil slicks, the surface tension of seawater is increased and the surface wave motion is significantly depressed. This effect relatively reduces the sea surface roughness, decreases the radar backscattered energy and enables oil slicks to be discernible from the radar image. Several algorithms may be applied for local fractal dimension estimation, but most solutions are tailored for specific applications and are characterized by estimation accuracies depending on the adopted image model and also on the value being estimated. This paper describes a decision-based fusion approach for local fractal dimension estimation of SAR images of the sea surface. Three different estimation algorithms are considered and the three resulting fractal maps are fused by means of a weighted average. The weights are calculated from the performance characteristics of the three algorithms measured on synthetic fractal surfaces. The experimental results carried out on ERS-2 SAR images prove the effectiveness of the proposed decision-based fusion approach

The aim of This work is to define an analysis technique, which uses the fractionally integrated autoregressive-moving average (FARIMA) model, for discriminating wind falls from oil slick areas in sea SAR imagery. The method deals with the estimation of the fractional differencing and ARMA parameters for the sea SAR images mean radial PSD and is applied to some ERS-1 and ERS-2 images of the Mediterranean sea, North sea and Atlantic (Galicia) ocean containing only oil slick or only wind falls, or wind falls and oil slick anomalies.

The aim of this paper is to investigate whether fractal maps extracted from sea SAR images are useful for discriminating the sea from other entities or anomalies. Fractal mapping consists of locally estimating the fractal dimension of the image. To this purpose four different methods based on covering and spectral analysis are proposed and compared when applied to real ERS1-2 GEC images. Wind falls, sea and line coast are well distinguishable in the fractal maps. This result clearly shows that the use of image fractal processing is a promising and powerful technique for identifying sea surface anomalies.

In this paper the authors analyze the correlations between sea surface roughness, wind intensity and radar backscattering time series, by using the fractal geometry. The aim is to provide evidence, by using real data, of correlation between the sea surface roughness and the backscattering fractal dimension. The results have highlighted good correlations and shown the usefulness of the fractal dimension as a parameter for sea surface roughness characterization.

Fractal dimension of the sea surface is strictly related to its roughness, and may thus be helpful in determining the sea state, or where motion-damping oil spills are located. A possible way to determine the fractal dimension of the sea surface is that of performing a fractal analysis of remote sensing images, in particular satellite images, which have the advantage of observing a large area at one time. This paper aims to show the utility of fractal analysis of ERS-1 SAR images, and presents the results obtained by three different algorithms. The considered data was sensed by ERS-1 in the Mediterranean Sea at times and locations suitable for comparison with data coming from the Italian "Sistema Ondametrico Nazionale," an environmental measurement system including a number of buoys carrying accelerometers and communications instruments. The experimental results show some accordance between the buoy data and the ERS-1 fractal analysis outcome, but more data are required to provide statistical support to the conclusions.

This paper proposes a spectral analysis algorithm for sea SAR image fractal dimension estimation. The power spectral density of the sea SAR image is obtained through the periodogram approach and by decomposing the spectrum into sub-bands according to a dyadic scale. The fractal dimension of the image is obtained by a linear regression of the absolute maxima of each sub-band. This method can be applied globally or locally to have a single value or a map of the image fractal dimension. Examples on simulated and real data are reported.

Very high resolution inverse synthetic aperture radar (ISAR) imaging of maneuvering targets is a complicated task. In fact, the conventional range Doppler (RD) ISAR technique does not work properly when target motions generate terms higher than the first order in the phase of the received signal relative to each scatterer. This effect typically happens when at least one of these situations occur: (1) very high resolution images are required; (2) the target maneuvers; and (3) the target undergoes significant angular motions (roll, pitch, and yaw). A novel ISAR technique, named range instantaneous Doppler (RID), has been proposed for the reconstruction of very high resolution images of maneuvering targets. In this paper, we analytically show that the RID technique works properly when high-resolution ISAR images are required of maneuvering and/or rolling, pitching, and yawing targets; we also quantify the performance improvement of the RID technique with respect to the RD technique. The problem is tackled from an analytical point of view. First, we define a new model of the ISAR received signal that is valid for maneuvering targets, then we derive and compare the analytical expression of the point spread function (PSF) for the two techniques. Furthermore, we perform a statistical analysis to evaluate the improvement of the RID technique versus the RD technique in terms of spatial resolution. Finally, we prove the effectiveness of the RID technique by simulating the imaging process for two different targets: (1) a ship that undergoes roll, pitch and yaw motions and (2) a fast maneuvering airplane.

In this paper, the problem of microwave imaging of rotating objects is approached from a digital signal processing point of view. Attention is focused on the reconstruction of the two-dimensional (2-D) image of an object obtained by processing complex samples of the continuous wave (CW) signal return received for different object aspect angles covering a 2pi total variation. The effects of a polar sampling of the Fourier transform (FT) of the target reflectivity function on the impulse response of the imaging system are analyzed. A criterion for the choice of the angular sampling interval in order to control the image blurring is given. Finally, a new and fast digital signal processing method for image reconstruction is proposed. This method reconstructs high resolution images of rotating targets illuminated by a CW electromagnetic signal or by a large bandwidth transmitted signal. This algorithm is suitable for processing wideband received signal of targets observed for a large variation of the viewing angle, so it is very attractive for the reconstruction of very high spatial resolution target images in SAR/ISAR systems. To show the performance of the method we finally present and discuss the results obtained by applying the proposed technique on simulated and experimental data.

In this paper we present a SAR/ISAR image autofocusing technique based on bispectrum analysis. The noncompensated SAR/ISAR imaging system can be modeled as a bandlimited complex linear time invariant (LTI) filter, so the autofocusing problem is equivalent to the problem of reconstructing the phase of a bandlimited complex LTI system. The complex transfer function of the imaging system is recovered from the estimated bispectrum of the received data by means of an ad-hoc algorithm. The advantage of this technique with respect to those based on the time-frequency analysis is that it allows to compensate for the phase term due to the motion of the radar with respect to the target also in the absence a dominant scatterer in the observed scene.