Multiband SAR classification using contextual analysis: annealing segmentation vs. a neural kernel-based approach

In this paper we derive two techniques for the classification of multipolarimetric/multifrequency SAR images, based respectively on a statistical and on a neural approach. Both techniques are especially designed to exploit of the spatial structure of the observed scene, thus identifying homogeneous regions that can be jointly classified. Such techniques are useful when looking at medium to large scale features, like the boundaries between urban and non-urban areas. They are applied to a set of multipolarimetric/multifrequency SIRC images of a urban area, to test their effectiveness in the identification of built up areas. A quantitative comparison of the results achievable with the two techniques is carried out, showing a similar behavior, even if the statistical approach tends to achieve better performance.