Restoration of polarimetric SAR images using simulated annealing

Filtering synthethic aperture radar (SAR) images ideally results in better estimates of the parameters characterizing the distributed targets in the i mages while preserving the structures of the nondistributed targets. Howeve r, these objectives are normally conflicting, often leading to a filtering approach favoring one of the objectives. An algorithm for estimating the ra dar cross-section (RCS) for intensity SAR images has previously been propos ed in the literature based on Markov random fields and the stochastic optim ization method simulated annealing. A new version of the algorithm is prese nted applicable to multilook polarimetric SAR images, resulting in an estim ate of the mean covariance matrix rather than the RCS. Small windows are ap plied in the filtering, and due to the iterative nature of the approach, re asonable estimates of the polarimetric quantities characterizing the distri buted targets are obtained while at the same time preserving most of the st ructures in the image. The algorithm is evaluated using multilook polarimet ric L-band data from the Danish airborne EMISAR system, and the impact of t he algorithm on the unsupervised H-alpha classification is demonstrated.