Comparison of annealing and iterated lters for speckle reduction in SAR

Many of the despeckling lters currently available (Mean, Median, Lee Sigma, Lee MMSE, Kuan, Frost, etc.) operate by smoothing over a xed window, whose size must be decided by two competing factors. Over homogeneous regions large window sizes are needed to improve speckle reduction by averaging. However, a large window size reduces the fundamental resolution of the algorithm, as with multi-looking. For instance, when one of these lters attempt to reconstruct a small bright object it produces artifacts around the object over a distance equal to the lter dimension. This means that the background is badly deened in the neighbourhood of bright targets and edges, which is just where one would like it accurate. In this paper, these problems are overcome by introducing a correlated neighbourhood model into the MAP lter. This lter operates on a small window and so is able to preserve resolution. The correlation model allows us to describe both the scene heterogeneity and the eeects of partial smoothing, which in turn, allows us to iterate the lter, hence, increasing the amount of smoothing that can be achieved with a small window. This gives a lter that is able to adapt to the underlying uctuations of the scene, preserve detail and still achieve large amounts of smoothing. The nal iterated lter is then compared with the current DRA simulated annealing algorithm.