In this work, spectral estimation techniques are used to exploit baseline diversity of a multichannel interferometric synthetic aperture radar (SAR) system and overcome the layover problem. This problem arises when different height contributions collapse in the same range-azimuth resolution cell, due to the presence of strong terrain slopes or discontinuities in the sensed scene. We propose a multilook approach to counteract the presence of multiplicative noise, which is due to the extended nature of natural targets; to this purpose we extend the RELAX algorithm to the multilook data scenario (M-RELAX). A thorough performance analysis of nonparametric (beamforming and Capon) and parametric (root MUSIC and M-RELAX) techniques is carried out based on Monte Carlo simulations and Cramer-Rao lower bounds (CRLB) calculation. The results suggest the superiority of parametric methods over nonparametric ones.

Layover solution in multibaseline SAR interferometry

Synthetic aperture radar interferometry (InSAR) is a powerful and increasingly expanding technique for measuring the topography of a surface, its changes over both short- and long-time scale, and other changes in the detailed characteristics of the surface. We provide a tutorial description of recent results on multibaseline (MB) InSAR processing. The main focus is on the problem of retrieving both heights and radar reflectivities of natural layover areas by means of a cross-track InSAR (XTI-SAR) system with a uniform linear array (ULA). It is formulated as the problem of detecting and estimating a multicomponent signal corrupted by multiplicative noise - the speckle in the radar imaging jargon - and by additive white Gaussian noise. Application to the InSAR problem of both nonparametric and parametric modern spectral estimation techniques is described. The problem of estimating the number of signal components in the presence of speckle is also addressed. Finally, a brief mention is given to recent research trends on robust methods for nonperfectly calibrated arrays, on processing for non-ULA configurations, and on MB SAR tomography, which is an extension of MB SAR interferometry for the full 3D mapping of semitransparent volume scattering layers. The state of the art of other advanced multichannel interferometric techniques is also briefly recalled.

Multibaseline cross-track SAR interferometry: a signal processing perspective

In recent years, there has been great interest in exploiting the advanced multibaseline operation of synthetic aperture radar interferometry (InSAR) for solving layover effects from complex orography, which can degrade both SAR and InSAR imagery of terrain radar reflectivity and height. In this work, the problem of retrieving radar reflectivity of layover areas is addressed. It is formulated as the problem of estimating a multicomponent sinusoidal signal corrupted by multiplicative complex correlated noise and additive white Gaussian noise. Application of nonparametric [e.g., Capon, amplitude and phase estimation filter (APES)], parametric [least squares, modern parametric RELAXation spectral estimator (RELAX)], and hybrid spectral estimators for amplitude estimation is investigated for a multilook scenario. In particular, the multilook extensions of RELAX and APES are applied to the interferometric problem. Performance analysis is investigated through a Cramer-Rao lower bound calculation and Monte Carlo simulation. The method of least squares, coupled with Capon's approach to spatial frequency estimation, multilook APES, and multilook RELAX turn out to provide accurate reflectivity estimates for undistorted multibaseline image formation of layover areas.

Reflectivity estimation for multibaseline interferometric radar imaging of layover extended sources

Synthetic aperture radar (SAR) imaging is a valuable tool in a number of defense surveillance and monitoring applications. There is increasing interest in 3-D reconstruction of objects from radar measurements. Traditional 3-D SAR image formation requires data collection over a densely sampled azimuth-elevation sector. In practice, such a dense measurement set is difficult or impossible to obtain, and effective 3-D reconstructions using sparse measurements are sought. This paper presents wide-angle 3-D image reconstruction approaches for object reconstruction that exploit reconstruction sparsity in the signal domain to ameliorate the limitations of sparse measurements. Two methods are presented; first, we use lp penalized (for p ≤ 1) least squares inversion, and second, we utilize tomographic SAR processing to derive wide-angle 3-D reconstruction algorithms that are computationally attractive but apply to a specific class of sparse aperture samplings. All approaches rely on high-frequency radar backscatter phenomenology so that sparse signal representations align with physical radar scattering properties of the objects of interest. We present full 360° 3-D SAR visualizations of objects from air-to-ground X-band radar measurements using different flight paths to illustrate and compare the two approaches.

Sparse Signal Methods for 3-D Radar Imaging

X-ray phase contrast imaging (XPCI) is more sensitive to density variations than X-ray absorption radiography, which is a crucial advantage when imaging weakly-absorbing, low-Z materials, or steep density gradients in matter under extreme conditions. Here, we describe the application of a polychromatic X-ray laser-plasma source (duration ~0.5 ps, photon energy >1 keV) to the study of a laser-driven shock travelling in plastic material. The XPCI technique allows for a clear identification of the shock front as well as of small-scale features present during the interaction. Quantitative analysis of the compressed object is achieved using a density map reconstructed from the experimental data.

/pdf/quantitative-phase-contrast-imaging-of-a-shock-wave-with-a-10zq75rz1g.pdf

Quantitative phase contrast imaging of a shock-wave with a laser-plasma based X-ray source

The authors consider the problem of synthetic aperture radar (SAR) imaging of 3D targets, given a limited amount of measured data. They explore an image reconstruction method proposed by Webb and Munson. This approach employs interpolation and a series of 2D inverse FFTs to cast the 3D imaging problem into the form of a high-resolution spectral estimation problem. The series of 1D spectral estimation problems can be solved using a variety of methods. The authors apply two of those methods: ESPRIT and RELAX to simulated 3D data for a maximum of 2 and 4 elevations. They also compare the performance of the image reconstruction algorithms with the Cramer-Rao lower bound.

http://ieeexplore.ieee.org/iel4/5660/15176/00699537.pdf

Spotlight-mode SAR imaging of a three-dimensional scene using spectral estimation techniques

We propose a new fast Feldkamp algorithm for 3-D cone beam tomography with a circular source trajectory. The algorithm is an extension of our recent fast native 2-D fan-beam reconstruction algorithm. It is based on a recursive hierarchical decomposition of the cone-beam backprojection operation into successively smaller sub-volumes. The algorithm reduces the computational complexity of the reconstruction from O(N/sup 4/) to O(N/sup 3/ log N). Simulations demonstrate the efficiency of our algorithm, with 7-fold speedup for a 128 /spl times/ 128 /spl times/ 128 image. Speedups will be much greater for images of more typical size encountered in medicine.

/pdf/fast-feldkamp-algorithm-for-cone-beam-computer-tomography-2yrqo2os1d.pdf

Shu Xiao

Papers

Spotlight-mode SAR imaging of a three-dimensional scene using spectral estimation techniques

Fast Feldkamp algorithm for cone-beam computer tomography