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.

Interferometric synthetic aperture radar (InSAR) systems allow the estimation of the height profile of the Earth surface. When the height profile of the observed scene is characterized by high slopes or exhibits strong height discontinuities, the height reconstruction obtained from a single interferogram is ambiguous, since the solution of the estimation problem is not unique. To solve this ambiguity and restore the solution uniqueness, multiple interferograms, obtained with different baselines and/or with different frequencies, have to be used (multichannel InSAR). The height profile can then be estimated from multiple interferograms using maximum likelihood (ML) estimation techniques or by means of maximumaposteriori(MAP) estimation techniques, which take into account the relation between adjacent pixels. In this paper, the height estimation accuracy achievable with a given multibaseline interferometric configuration and using the aforementioned estimation techniques in terms of Cramer-Rao lower bound for the ML and of error lower bound for the MAP, is analyzed and discussed. It is shown that the MAP technique outperforms the ML one and that its attainable accuracy is not sensitive to the baselines choice, while mainly depends on the ground slopes.

Multichannel interferometric synthetic aperture radar (InSAR) systems allow the estimation of the height profile of the Earth's surface, exploiting the availability of multiple radar acquisitions, obtained via different baselines/frequencies. Statistical approaches, in particular maximumaposterioritechnique 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.

Interferometric SAR (InSAR) systems are able to estimate height profiles of the Earth surface. For the involved estimation problem, Maximum A Posteriori (MAP) statistical technique and Markov Random Field image models have been used, showing to be effective in case of multiple interferograms, obtained via different baselines/frequencies. In this paper, we face the problem of unknown phase offsets presence affecting real interferograms, which makes impossible to retrieve correct height estimations. We present a procedure to estimate these offset values, based on statistical estimation and we test it both on simulated and real data, showing the effectiveness of the method.

In ad hoc networks scalability is a critical requirement if these technologies have to reach their full potential. Most of the proposed routing protocols do not operate efficiently with networks of more than a few hundred nodes. In this paper, we propose an augmented tree-based address space structure and a hierarchical multi-path routing protocol, referred to as augmented tree-based routing (ATR), which utilizes such a structure in order to solve the scalability problem and to gain good resilience against node failure/mobility and link congestion/instability. Simulation results and performance comparisons with existing protocols substantiate the effectiveness of the ATR.

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.

SAR images are affected by speckle that affects radiometric resolution and class discrimination capabilities. Recently, different speckle reduction techniques based on maximum a posteriori (MAP) estimation have been proven to have very good performances. These techniques are based on the introduction of an a priori statistical model of the speckle free image to be estimated. We propose a MAP method using more than one sub-band filtered intensity images and a Markov random field (MRF) a priori model. The method has been experimented on simulated and real images

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

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.

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 aimulated 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.

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.

The application of a maximum a posteriori estimation method for microwave imaging that makes use of a Markov random field (MRF) a priori statistical model is presented. In particular, the MRF family adopted is generalized for complex profiles, characterized by a quadratic energy function and "modified" such to make it possible to statistically represent spatial interactions between real and imaginary parts. Thanks to its peculiarities, the use of this approach simultaneously favors in many cases well posedness and robustness against local minima occurrence, as well as high quality in the reconstructed images. Numerical results show the performance of the method.

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.

Presents a new technique, based on a MAP (maximum a posteriori probability) approach, for filtering interferometric phase images. To describe prior knowledge about the unknown image, a MRF/Gibbs model is adopted, and its energy function is defined so as to impose two desirable constraints on the solution: the absence of residues (which leads to unambiguous phase unwrapping) and the smoothness of the phase field (which reduces noise). MAP estimation is carried out by simulated annealing, obtaining accurate results even for low values of the coherence.

The application of a Markov random field (MRF) based maximum a posteriori (MAP) estimation method for microwave imaging, in a non-linear framework, is presented. The adopted MRF family is characterized by a quadratic energy function, and is "modified" such that it statistically represents complex profiles. This approach simultaneously allows, in many cases, well posedness and robustness against local minima occurrence.

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.