Fusion of airborne MTIonSAR and ancillary information for vehicle tracking

The focus of this paper is on resolving some of the ambiguity inherent in locating moving targets in single-channel SAR images. Detection of moving targets relies on a technique named doublet detection, while tracking is based on velocity estimation in the range-Doppler plane; confirmation of the target as a mover is enabled by two specifically developed tests on the range-Doppler target path. Velocity estimation is performed through a theoretically derived formula. Comparison of the estimated target parameters with the road pattern as provided by a GIS containing the local road network is relied upon for target re-location. The method and the results of testing on real images are presented and discussed

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

n 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