Vito Pascazio

A nonlinear estimation method in tomographic imaging

A nonlinear estimation approach to solving the inverse scattering problem, and reconstructing the space-varying complex permittivity of unknown objects is considered. The bilinear operator equations governing the scattering are approximated into finite dimensional spaces on the basis of the finite degrees of freedom of data, and on the simple concept that one cannot expect to reconstruct an arbitrary function from a finite number of independent equations. As a consequence, a discrete model, well suited to numerical inversion, is developed. The particular bilinear nature of the equations, and a suitable choice of contrast and field unknowns allows the functional adopted in the estimation to be minimized in an accurate and numerically efficient manner. Numerical experiments show how the method is capable, when a proper number of searched unknowns is adopted, to manage the possible convergence to local minima (which is a typical question in nonlinear inverse problems), and validate the effectiveness of the proposed approach.

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.

Subsurface inverse scattering problems: quantifying, qualifying, and achieving the available information

In inverse scattering problems, only a limited amount of independent data is actually available whenever the finite accuracy of the measurement set up is taken into account. In this paper, we deal with the problem of quantifying such an amount in the subsurface sensing case. In particular, an alternative formulation of the problem is given which also allows to understand how to dimensionate the measurement setup in an optimal fashion. Analytical results are reported for the case of a lossless soil, while a numerical study is carried out in the general case. By relying on the same formulation and tools, we also discuss the kind of unknown profiles that can actually be retrieved. In particular, it is shown that the class of retrievable functions exhibits intrinsic multiresolution features. This suggests that adoption of wavelet expansions to represent the unknown function may enhance the reconstruction capabilities. Numerical examples support this conclusion.

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.

Multichannel Phase Unwrapping With Graph Cuts

Markovian approaches have proven to be effective for solving the multichannel phase-unwrapping (PU) problem, particularly when dealing with noisy data and big discontinuities. This letter presents a Markovian approach to solve the PU problem based on a new a priori model, the total variation, and graph-cut-based optimization algorithms. The proposed method turns out to be fast, simple, and robust. Moreover, compared with other approaches, the proposed algorithm is able to unwrap and restore the solution at the same time, without any additional filtering. A set of experimental results on both simulated and real data illustrates the effectiveness of our approach.

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.

Multichannel SAR interferometry via classical and Bayesian estimation techniques

Some multichannel synthetic aperture radar interferometric configurations are analyzed. Both across-track and along-track interferometric systems, allowing to recover the height profile of the ground or the moving target radial velocities, respectively, are considered. The joint use of multichannel configurations, which can be either multifrequency or multi-baseline, and of classical or Bayesian statistical estimation techniques allows to obtain very accurate solutions and to overcome the limitations due to the presence of ambiguous solutions, intrinsic in the single-channel configurations. The improved performance of the multichannel-based methods with respect to the corresponding single-channel ones has been tested with numerical experiments on simulated data.