Segmentation and simulated annealing

In this paper we present a new algorithm for segmenting SAR images. A common problem with segmentation algorithms for SAR imagery is the poor placement of the edges of regions and hence of the regions themselves. This usually arises because the algorithm considers only a limited number of placements for regions. The new algorithm circumvents this shortcoming, and produces an optimal segmentation into a prescribed number of regions. An objective function is derived from a statistical model of SAR imagery. This objective function is then minimized by the method of simulated annealing which is, assuming some weak constraints, guaranteed to give the global minimum. Starting with an initial segmentation, the algorithm proceeds by randomly changing the current state. The annealing then decides whether or not to accept the new configuration by calculating the difference between the likelihoods of the data fitting these segmentations. In practice there are many possible implementations of the algorithm. We describe an implementation which uses a free topological model and alters the segmentation on a pixel by pixel basis. This makes it possible to get results of high resolution, as shown in results obtained by applying the new algorithm to both airborne X-band and ERS1 imagery.