Gilda Schirinzi

SARAS: a synthetic aperture radar (SAR) raw signal simulator

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

Three-Dimensional SAR Focusing From Multipass Signals Using Compressive Sampling

Three-dimensional synthetic aperture radar (SAR) image formation provides the scene reflectivity estimation along azimuth, range, and elevation coordinates. It is based on multipass SAR data obtained usually by nonuniformly spaced acquisition orbits. A common 3-D SAR focusing approach is Fourier-based SAR tomography, but this technique brings about image quality problems because of the low number of acquisitions and their not regular spacing. Moreover, attained resolution in elevation is limited by the overall acquisitions baseline extent. In this paper, a novel 3-D SAR data imaging based on Compressive Sampling theory is presented. It is shown that since the image to be focused has usually a sparse representation along the elevation direction (i.e., only few scatterers with different elevation are present in the same range-azimuth resolution cell), it suffices to have a small number of measurements to construct the 3-D image. Furthermore, the method allows super-resolution imaging, overcoming the limitation imposed by the overall baseline span. Tomographic imaging is performed by solving an optimization problem which enforces sparsity through ℓ1-norm minimization. Numerical results on simulated and real data validate the method and have been compared with the truncated singular value decomposition technique.

A SAR processor based on two-dimensional FFT codes

A synthetic aperture radar (SAR) processor approach based on two-dimensional fast Fourier transform (FFT) codes coupled with an asymptotic evaluation of the unit response function is presented. For the latter, no approximation is made to the distance function, so that the full range of geometric aberrations is analytically considered, enabling an effective reference filter to be designed. The two-dimensional FFTs were designed as to run on computers of very limited memory: the required FFT is computed by means of FFTs of lower order. Two FFT codes were considered: one is faster and allows full or reduced (quick look or multilook) resolution performance to be obtained easily; the second is slower but allows the use of a space-varying filter and/or investigations on limited portions (zoom) of the image. Both codes are suited to parallel processing, e.g. by a transputer net. A full discussion on computer memory and time requirements is presented as well as first examples of image processing results. >

Maximum a posteriori estimation of height profiles in InSAR imaging

We present a statistical method to solve the height estimation problem in interferometric synthetic aperture radar (InSAR) applications. It is based on the use of multifrequency SAR raw datasets obtained by partitioning in subbands the available raw data spectrum, and on a Bayesian estimator using Markov random fields to model the a priori distribution of the unknown images. The method allows recovering topographic profiles affected by strong height discontinuities and allows to perform efficient noise rejections.

Multifrequency InSAR height reconstruction through maximum likelihood estimation of local planes parameters

In this paper, a technique that is able to reconstruct highly sloped and discontinuous terrain height profiles, starting from multifrequency wrapped phase acquired by interferometric synthetic aperture radar (SAR) systems, is presented. We propose an innovative unwrapping method, based on a maximum likelihood estimation technique, which uses multifrequency independent phase data, obtained by filtering the interferometric SAR raw data pair through nonoverlapping band-pass filters, and approximating the unknown surface by means of local planes. Since the method does not exploit the phase gradient, it assures the uniqueness of the solution, even in the case of highly sloped or piecewise continuous elevation patterns with strong discontinuities.

Estimation of Radial Velocity of Moving Targets by Along-Track Interferometric SAR Systems

Along-track interferometric synthetic aperture radar (AT-InSAR) can be used to estimate the radial velocity of ground moving targets, starting from interferometric phase measures. The estimation obtained from a single-phase interferogram suffers from ambiguities. To solve these problems, multichannel AT-InSAR systems are required. In this letter, we analyze the radial velocity maximum-likelihood estimation accuracy with respect to AT-InSAR system parameters, such as velocity values and different clutter and thermal noise levels. We consider two different models for the target response: a deterministic model and a zero-mean Gaussian model. The presented results show that AT-InSAR systems exhibit better estimation accuracies for low-velocity values (slow targets).

DEM Reconstruction Accuracy in Multi-Channel SAR Interferometry

Interferometric SAR (InSAR) systems allow the estimation of the height profile of the Earth surface. Maximum Likelihood (ML) and Maximum A Posteriori (MAP) statistical techniques have shown to be effective for such problem if multiple interferograms, obtained with different baselines and/or with different frequencies, are used (multi-channel InSAR). In this paper, we evaluate the reconstruction performance of the considered ML and MAP statistical height estimation methods in terms of the Cramer-Rao Lower Bounds (CRLB) of the estimated height values.

Estimation of terrain elevation by multifrequency interferometric wide band SAR data

We present a phase unwrapping method using a maximum likelihood estimation technique together with frequency diversity information to reconstruct highly discontinuous ground elevation profiles. Frequency diversity can be obtained by considering the interferograms obtained by different couples of subband images.

SAR tomography from sparse samples

Three dimensional (3-D) Synthetic Aperture Radar (SAR) image formation provides the scene reflectivity estimation along azimuth, range and elevation co-ordinates. For 3-D image focusing multiple signals, acquired along different orbits, are required. The practical application of the focusing methods requires that non-uniformly spaced acquisition orbits have to be considered. In this paper we propose a technique exploiting the Compressive Sampling theory, and assuming that the image to be focused has a sparse representation along the elevation directions, which amounts to suppose that only few point-like scatterers with different elevation are present in the same range-azimuth resolution cell. Numerical results on simulated data show the good performance of the method.

Iterative homomorphic technique for speckle reduction in synthetic-aperture radar imaging

The presence of speckle in radar images reduces the radiometric resolution and renders less efficient the procedures for texture class discrimination. We present an algorithm devoted to speckle reduction in syntheticaperture radar images based on a homomorphic filter coupled with a Wiener filter. To construct the Wiener filter we analytically evaluate the autocorrelation function of the noise, starting from the first two orders of statistics of the noise, before performing the homomorphic transformation (a logarithmic one, in the case of multiplicative noise), and the autocorrelation of the noise-free image is evaluated by an iterative procedure. The algorithm, tested on both simulated and actual synthetic-aperture radar images, provides very promising results and shows the usefulness of the proposed method.