SAR Image Regularization With Fast Approximate Discrete Minimization

ABSTRACT:

Synthetic aperture radar (SAR) images, like other coherent imaging modalities, suffer from speckle noise. The presence of this noise makes the automated interpretation of pictures a challenging task and noise reduction is typically a requirement 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 approach to specific both data fidelity constraints and fascinating 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 serious-tailed distributions, and therefore the MRF formulation leads to a minimization problem involving nonconvex log-chance terms. Such a minimization will be performed efficiently by computing minimum cuts on weighted graphs. Due to memory constraints, exact minimization, although theoretically doable, isn't achievable on massive pictures required by remote sensing applications. The computational burden of the state-of-the-art algorithm for approximate minimization (particularly the -growth) is simply too serious specially when considering joint regularization of many pictures. We have a tendency to 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 section in urban space SAR pictures.

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