Multibaseline cross-track SAR interferometry: a signal processing perspective

Abstract

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 of the research activity at the University of Pisa 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. State of the art of other advanced multichannel interferometric techniques is also briefly recalled.

Three-Dimensional Imaging and Scattering Mechanism Estimation Over Urban Scenes Using Dual-Baseline Polarimetric InSAR Observations at L-Band

“…An established signal model of single-polarization MB InSAR data with p antennas [24]- [26] contains the SAR speckle phenomenon as multiplicative noise. For extended sources, the measurement vector y(l) is expressed as…”

“…, L, and the Schur-Hadamard product (elementwise multiplication). The term c(l) incorporates the SAR speckle effect [24]- [26] as multiplicative noise x j (l) of source j and represents the response of natural targets. The additive noise is denoted by n(l), and the steering vector is denoted by a(θ j ).…”

“…Recently, array signal processing methods for analyzing single-polarization MB InSAR measurements have been investigated exhaustively by simulations [24]- [26]. They estimate the signal parameters like their spatial frequencies and reflectivities.…”

Urban Area Tomography Using a Sparse Representation Based Two-Dimensional Spectral Analysis Technique

“…However, a problem that occurs is that the range resolution improvement influences the phase information, which results in incorrect inversion results for elevation. Several TomoSAR methods exist: nonparametric spectral analysis methods, like Fourier beamforming and Capon [4,5], are fast and robust in focusing artifacts, whereas parametric spectral analysis methods such as multiple signal classification (MUSIC) [6], truncated singular value decomposition (TSVD) [7], and weighted subspace fitting (WSF) estimators [8] obtain better vertical resolution. The compressive sensing-based method not only provides a good compromise between the parametric and nonparametric spectral analysis methods, but can achieve superior resolution with a small number of acquisitions and unevenly spaced orbits [9][10][11][12][13][14][15][16][17][18].…”

Multibaseline polarimetric synthetic aperture radar tomography of forested areas using wavelet-based distribution compressive sensing

“…There are several spectral analysis techniques used to perform tomography, such as beamforming, Capon, 2,3 multiple signal classification, 4 truncated singular value decomposition, 4 and weighted subspace fitting estimators. 5 However, in practice, due to unevenly sampled acquisitions, infrequent passes over the area of interest, and limited overall acquisition baseline extent, these techniques generally bring about some quality problems in the estimation accuracy of the positions of scatterers and the resolution power such that scatterers can be discriminated.…”