Synthetic aperture radar interferometry is an imaging technique for measuring the topography of a surface, its changes over time, and other changes in the detailed characteristic of the surface. By exploiting the phase of the coherent radar signal, interferometry has transformed radar remote sensing from a largely interpretive science to a quantitative tool, with applications in cartography, geodesy, land cover characterization, and natural hazards. This paper reviews the techniques of interferometry, systems and limitations, and applications in a rapidly growing area of science and engineering.

Satellite radar interferometry (SRI) provides an important new tool for determining ice-flow velocity. Interferometric measurements made from a single-track direction are sensitive only to a single component of the three-component velocity vector. Observations from along three different track directions would allow the full velocity vector to be determined. A north/south-looking synthetic aperture radar (SAR) could provide these observations over large portions of the globe, but not over large areas of the polar ice sheets. The authors develop and demonstrate a technique that allows the three-component velocity vector to be estimated from data acquired along two track directions (ascending and descending) under a surface-parallel flow assumption. This technique requires that there are accurate estimates of the surface slope, which are also determined interferometrically. To demonstrate the technique, the authors estimate the three-component velocity field for the Ryder Glacier, Greenland. Their results are promising, although they do not have yet ground-truth data with which to determine the accuracy of their estimates.

Through-the-Earth (TE) propagation subjects a radio signal to considerable attenuation. The uplink signal must, however, be detected in the presence of full-strength atmospheric noise. Consequently, through-the-Earth communication systems are generally restricted to relatively low data rates. The electromagnetic characteristics of the TE signal differ from those of the surface-propagating noise. In addition, much of the energy in low-frequency noise is contained in discrete impulses. This paper describes an integrated combination of adaptive noise cancellation (ANC), maximum-likelihood detection (MLD), nonlinear processing (NLP), and decision-aided feedback (DAFB) that can be used to combat atmospheric noise in TEC systems. Tests with real VLF noise demonstrate a 10-to-24 dB improvement in SNR. The reduced effective noise level allows higher data rates, greater depth of operation, smaller transmitters, and/or smaller antennas.

Derives closed-form expressions for the probability density functions (PDF's) for copolar and cross-polar ratios and for the copolar phase difference for multilook polarimetric SAR data, in terms of elements of the covariance matrix for the backscattering process. The authors begin with the case in which scattering-matrix data are jointly Gaussian-distributed. The resulting copolar-phase PDF is formally identical to the phase PDF arising in the study of SAR interferometry, so the authors' results also apply in that setting. By direct simulation, they verify the closed-form PDF's. They show that estimation of signatures from averaged covariance matrices results in smaller biases and variances than averaging single-look signature estimates. They then generalize their derivation to certain cases in which backscattered intensities and amplitudes are K-distributed. They find in a range of circumstances that the PDF's of polarimetric signatures are unchanged from those derived in the Gaussian case. They verify this by direct simulation, and also examine a case that fails to satisfy an important assumption in their derivation. The forms of the signature distributions continue to describe data well in the latter case, but parameters in distributions fitted to (simulated) data differ from those used to generate the data. Finally, the authors examine samples of K-distributed polarimetric SAR data from Arctic sea ice and find that their theoretical distributions describe the data well with a plausible choice of parameters. This allows the authors to estimate the precision of polarimetric-signature estimates as a function of the number of SAR looks and other system parameters.>

The K distribution has proven to be a promising and useful model for backscattering statistics in synthetic aperture radar (SAR) imagery. However, most studies to date have relied on a method of moments technique involving second and fourth moments to estimate the parameters of the K distribution. The variance of these parameter estimates is large in cases where the sample size is small and/or the true distribution of backscattered amplitude is highly non-Rayleigh. The present authors apply a maximum likelihood estimation method directly to the K distribution. They consider the situation for single-look SAR data as well as a simplified model for multilook data. They investigate the accuracy and uncertainties in maximum likelihood parameter estimates as functions of sample size and the parameters themselves. They also compare their results with those from a new method given by Raghavan (1991) and from a nonstandard method of moments technique; maximum likelihood parameter estimates prove to be at least as accurate as those from the other estimators in all cases tested, and are more accurate in most cases. Finally, they compare the simplified multilook model with nominally four-look SAR data acquired by the Jet Propulsion Laboratory AIRSAR over sea ice in the Beaufort Sea during March 1988. They find that the model fits data from both first-year and multiyear ice well and that backscattering statistics from each ice type are moderately non-Rayleigh. They note that the distributions for the data set differ too little between ice types to allow discrimination based on differing distribution parameters.>