Giancarlo Ferraiuolo

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.

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.

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.

Statistical regularization in linearized microwave imaging through MRF-based MAP estimation: hyperparameter estimation and image computation

The application of a Markov random fields (MRF) based maximum a posteriori (MAP) estimation method for microwave imaging is presented in this paper. The adopted MRF family is the so-called Gaussian-MRF (GMRF), whose energy function is quadratic. In order to implement the MAP estimation, first, the MRF hyperparameters are estimated by means of the expectation-maximization (EM) algorithm, extended in this case to complex and nonhomogeneous images. Then, it is implemented by minimizing a cost function whose gradient is fully analytically evaluated. Thanks to the quadratic nature of the energy function of the MRF, well posedness and efficiency of the proposed method can be simultaneously guaranteed. Numerical results, also performed on real data, show the good performance of the method, also when compared with conventional techniques like Tikhonov regularization.

Phase-Offset Estimation in Multichannel SAR Interferometry

Multichannel interferometric synthetic aperture radar (InSAR) systems allow the estimation of the height profile of the Earths surface, exploiting the availability of multiple radar acquisitions, obtained via different baselines/frequencies. Statistical approaches, in particular maximum a posteriori technique and Markov random-field image models, can be exploited for such estimation problem, which proved to be effective. However, despite the particular solution method used, the problem with multichannel interferometry is that interferograms can be affected from the presence of undetermined phase offsets, which makes it difficult to get correct height estimation in any case. In this letter, we present a procedure to estimate these phase offsets using statistical estimation; we test the procedure on both simulated and real data. For the latter, we show how an optimal estimation of the phase offsets can be used to improve the resolution of an available Shuttle Radar Topography Mission digital elevation model. The obtained results prove the effectiveness of the method and assess the overall quality of the height estimation procedure.

A Bayesian filtering technique for SAR interferometric phase fields

SAR interferograms are affected by a strong noise component which often prevents correct phase unwrapping and always impairs the phase reconstruction accuracy. To obtain satisfactory performance, most filtering techniques exploit prior information by means of ad hoc, empirical strategies. In this paper, we recast phase filtering as a Bayesian estimation problem in which the image prior is modeled as a suitable Markov random field, and the filtered phase field is the configuration with maximum a posteriori probability. Assuming the image to be residue free and generally smooth, a two-component MRF model is adopted, where the first component penalizes residues, while the second one penalizes discontinuities. Constrained simulated annealing is then used to find the optimal solution. The experimental analysis shows that, by gradually adjusting the MRF parameters, the algorithm filters out most of the high-frequency noise and, in the limit, eliminates all residues, allowing for a trivial phase unwrapping. Given a limited processing time, the algorithm is still able to eliminate most residues, paving the way for the successful use of any subsequent phase unwrapping technique.

Bayesian Regularization in Nonlinear Imaging: Reconstructions From Experimental Data in Nonlinearized Microwave Tomography

In this paper, we investigate the robustness and the effectiveness of a microwave imaging technique, based on the Bayesian estimation theory, for the reconstruction of dielectric profiles. The method has been applied and validated on real experimental data. Our statistical-based inversion algorithm takes advantage of Bayesian regularization, which permits the inversion of a strongly nonlinear model using a Markov random field as an a priori statistical model of the unknown image. Such choice leads to a robust and effective nonlinear inversion method. The exhaustive analysis performed on the experimental data shows the good performance of the method.

Maximum a posteriori height estimation in InSAR imaging

A multi-frequencies maximum a posteriori (MAP) estimation of height profiles, from InSAR data, is presented in this paper. A quadratic MRF model is adopted to exploit a-priori information about the unknown image; the hyperparameter estimation, performed on a local basis, provides a powerful representation model for realistic height surfaces. The resulting MAP estimation is efficiently performed by a Metropolis version of the simulated annealing algorithm, and is able to reconstruct very discontinuous profiles.

Unsupervised Bayesian reconstruction of microwave images from real data

We address the problem of non-linear microwave imaging for the reconstruction of dielectric profiles. We propose a statistical based inversion algorithm, adopting the Bayesian (MAP) framework and a complex Gaussian MRF model for the image. The use of statistical algorithms for the estimation of the complex MRF parameter leads to a robust and effective non-linear inversion method. Some experiments on real data are able to show the good performance of the method.

A Bayesian technique for terrain mapping using multi-frequency ground based interferometric SAR systems

In this paper we present some preliminary results of the application on real data of a statistical method to solve the height estimation problem in Interferometric Synthetic Aperture Radar (InSAR). The method is based on maximum a posteriori (MAP) estimation and Markov Random Fields (MRF) image modeling, and makes use of multifrequency/baseline SAR raw data. The real data set is acquired by a Ground-Based SAR (GB-SAR) interferometer based on the LiSA technology