Interferometric Synthetic Aperture Radar Phase Research Articles

Reducing ionospheric electron density errors in satellite radar interferometry applications

It has been demonstrated that ionospheric electron density disturbances can influence multi-pass satellite radar interferometry (SRI). This has been observed to occur near the magnetic poles, particularly during strong magnetic disturbances. Ionospheric disturbances most clearly appear in interferometric synthetic aperture radar (InSAR) as a kilometre-scale modulation in the azimuth pixel shift needed for optimal registration of the interferometric pair. This modulation (also known as "azimuth streaks") also affects the InSAR phase, and consequently such SRI applications as digital elevation model (DEM) generation and displacement measurement, and may appear as "streaks" in the coherence map. The effect is worse for L-band systems (e.g., Japanese Earth resources satellite 1 (JERS-1) and Phased array type L-band synthetic aperture radar (PALSAR)), but can be significant even at C band (e.g., European remote sensing satellites ERS-1/2 and RADARSAT I and II). The influence of ionospheric effects on interferogram phase can often be predicted, and the magnitude can be estimated, by an inspection of a map of the shift in azimuth position for optimal registration obtained using the "speckle tracking" technique. For DEM generation, the ionospheric phase contribution can be approximated, and removed from the interferogram phase, by the scaled and filtered integral of this azimuth shift map. For displacement-measurement applications, appropriate filtering of the distinct patterns of the azimuth streaks is the only resort.

SAR interferometric phase noise reduction using wavelet transform

The problem of synthetic aperture radar interferometric phase noise reduction is addressed. A new technique based on discrete wavelet transform is presented. This technique guarantees high resolution phase estimation without using phase image segmentation. Areas containing only noise are hardly processed. Tests with synthetic and real interferograms are reported.

Phase unwrapping for interferometric synthetic aperture radar by use of Helmholtz equation eigenfunctions and the first Green’s identity

We develop a new algorithm for interferometric synthetic aperture radar (SAR) phase unwrapping based on the first Green’s identity with the Green’s function representing a series in the eigenfunctions of the two-dimensional Helmholtz homogeneous differential equation. This provides closed-form solutions with use of one- or two-dimensional fast Fourier transforms. The algorithm is elaborated by using adaptive regularization of the interferometric phase gradient estimation. To diminish underestimation of the unwrapped phase typical of the linear phase unwrapping algorithms, the bias in the measured interferometric SAR phase is calculated in terms of the probability density function of the error in the processed interferometric SAR phase.

A new technique for noise filtering of SAR interferometric phase images

This paper addresses the noise filtering problem for synthetic aperture radar (SAR) interferometric phase images. The phase noise is characterized by an additive noise model. The model is verified with an L-band shuttle imaging radar (SIR)-C interferogram. An adaptive filtering algorithm based on this noise model is developed. It emphasizes filtering noise adaptively according to the local noise level and filtering along fringes using directionally dependent windows. This algorithm is effective, especially for the tightly packed fringes of X-band interferometry. Using simulated and SIR-C/X-SAR repeat-pass generated interferograms, the effectiveness of this filter is demonstrated by its capabilities in residue reduction, adaptive noise filtering, and its ability to filter areas with high fringe rates. In addition, a scheme of incorporating this filtering algorithm in iterative phase unwrapping using a least-squares method is proposed.

Interferometric SAR phase unwrapping using the finite element method

The authors present a new two-dimensional algorithm for interferometric synthetic aperture radar phase unwrapping based on the finite element method. The proposed technique allows an efficient least-squares solution carried out in time domain. Weighting functions can be introduced without increasing computational requirements. The possibility to speed up computations via a multigrid technique is discussed. A noise-robust extension of the proposed technique is also investigated. Presented experiments on real ERS-1 data, as well as on simulated patterns, demonstrate the precision and efficiency of the procedure.