Discrete target recognition in polarimetric SAR imagery

The recognition of discrete man-made targets in remotely sensed imagery is an important problem for strategic and tactical applications. The objective of this thesis is to examine whether the extra information content in polarimetric radar imagery will overcome the difficult problems in remotely sensed Synthetic Aperture Radar (SAR) data and improve target recognition capabilities with respect to single channel SAR. In conventional SAR these problems are mainly the result of speckle and receiver noise adversely affecting the limited information available in the single channel of data. To meet this objective, target samples from different target classes have been identified in real, polarimetric SAR data. These targets have a strong backscatter relative to the background clutter and are about three to six pixels in size. Target classes are denned by their polarization signature and available ground truth data. By exploiting the polarimetric properties of these targets it is possible to demonstrate an improvement in target detectability. A large number of measurements extracted from the polarimetric properties of scatterers are examined and reduced sets of these features have been selected. To discriminate identified targets in a SAR image, a supervised classification algorithm has been implemented. Optimal weighting of the feature sets to improve classification was not implemented due to the low confidence placed on the target feature distribution estimates as a result of the sparse training set. However, a comparison of classification results using the polarimetric data with trials performed on single channel SAR data synthesized from the same data set, clearly demonstrates a significant performance benefit of polarimetric radar. A polarimetric target model has been developed to estimate the sensitivity of the polarimetric classifier to several of the adverse properties of SAR polarimetry. Throughout this thesis, the observations are compared with other current research in this area and several related conclusions can be reached.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat