Open Access

Filtering of Interferometric SAR Phase Images as a Fuzzy Matching-Pursuit Blind Estimation

  • Bruno Aiazzi1Email author,
  • Stefano Baronti1,
  • Massimo Bianchini2,
  • Alessandro Mori2 and
  • Luciano Alparone2
EURASIP Journal on Advances in Signal Processing20052005:915321

https://doi.org/10.1155/ASP.2005.3220

Received: 6 August 2004

Published: 14 December 2005

Abstract

We present an original application of fuzzy logic to restoration of phase images from interferometric synthetic aperture radar (InSAR), which are affected by zero-mean uncorrelated noise, whose variance depends on the underlying coherence, thereby yielding a nonstationary random noise process. Spatial filtering of the phase noise is recommended, either before phase unwrapping is accomplished, or simultaneously with it. In fact, phase unwrapping basically relies on a smoothness constraint of the phase field, which is severely hampered by the noise. Space-varying linear MMSE estimation is stated as a problem of matching pursuit, in which the estimator is obtained as an expansion in series of a finite number of prototype estimators, fitting the spatial features of the different statistical classes encountered, for example, fringes and steep slope areas. Such estimators are calculated in a fuzzy fashion through an automatic training procedure. The space-varying coefficients of the expansion are stated as degrees of fuzzy membership of a pixel to each of the estimators. Neither a priori knowledge on the noise variance is required nor particular signal and noise models are assumed. Filtering performances on simulated phase images show a steady SNR improvement over conventional box filtering. Applications of the proposed filter to interferometric phase images demonstrate a superior ability of restoring fringes yet preserving their discontinuities, together with an effective noise smoothing performance, irrespective of locally varying coherence characteristics.

Keywords

adaptive filtering blind estimation coherence fuzzy logic interferometric synthetic aperture radar phase noise filtering

Authors’ Affiliations

(1)
Institute of Applied Physics "Nello Carrara" (IFAC), National Research Council (CNR)
(2)
Department of Electronics and Telecommunications (DET), University of Florence

Copyright

© Aiazzi et al. 2005