SAR image regularization with fast approximate discrete minimization

  • Authors:

  • Loïc Denis;Florence Tupin;Jérôme Darbon;Marc Sigelle

  • Affiliations:

  • École Supérieure de Chimie Physique Électronique de Lyon and Lab. Hubert Curien, CNRS UMR 5516, St-Étienne and Inst. TELECOM, TELECOM ParisTech, GET, Télécom Paris, C ...;Institut TELECOM, TELECOM ParisTech, GET, Télécom Paris, CNRS UMR, LTCI, Paris, France;Department of Mathematics, University of California, Los Angeles, CA;Institut TELECOM, TELECOM ParisTech, GET, Télécom Paris, CNRS UMR, LTCI, Paris, France

  • Venue:
  • IEEE Transactions on Image Processing
  • Year:
  • 2009

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Abstract

Synthetic aperture radar (SAR) images, like other coherent imaging modalities, suffer from speckle noise. The presence of this noise makes the automatic interpretation of images a challenging task and noise reduction is often a prerequisite for successful use of classical image processing algorithms. Numerous approaches have been proposed to filter speckle noise. Markov random field (MRF) modelization provides a convenient way to express both data fidelity constraints and desirable properties of the filtered image. In this context, total variation minimization has been extensively used to constrain the oscillations in the regularized image while preserving its edges. Speckle noise follows heavy-tailed distributions, and the MRF formulation leads to a minimization problem involving nonconvex log-likelihood terms. Such a minimization can be performed efficiently by computing minimum cuts on weighted graphs. Due to memory constraints, exact minimization, although theoretically possible, is not achievable on large images required by remote sensing applications. The computational burden of the state-of-the-art algorithm for approximate minimization (namely the -expansion) is too heavy specially when considering joint regularization of several images. We show that a satisfying solution can be reached, in few iterations, by performing a graph-cut-based combinatorial exploration of large trial moves. This algorithm is applied to joint regularization of the amplitude and interferometric phase in urban area SAR images.