In this paper, the problem of classifying nonhomogeneous man-made targets is investigated by performing a macroscopic and detailed target analysis. The Cloude-Pottier H/α ML decomposition is used as a starting point in order to find orientation-invariant feature vectors that are able to represent the average polarimetric structure of complex targets. A novel supervised classification scheme based on nearest neighbor decision rule is then designed, which makes use of the feature space. A validation process is performed by analyzing experimental data of simple targets collected in an anechoic chamber and airborne EMISAR images of eight ships. Three classification robustness performance indicators have been evaluated for each feature vector by performing the leaves-one-out-method described by Mitchell and Westerkamp. The robustness of the classifier has been tested with respect to the ability to reject unknown targets and to correctly identify known targets.
Sea Normalized RCS, and Doppler spectra have been revised for HF-OTH Clutter Modelling. The Hasselmann model is firstly introduced to predict the sea directional spectrum of fetch-limited sea and results have been compared with the Pierson-Moskovitz model used for large scale ocean remote sensing. Results show that the closed fetch-limited sea has lower NRCS compared with ocean for similar wind intensity and direction. For this reasons RCS and Doppler spectra must be predicted taking into account of the fetch dimension. In future work we will generalize this interesting approach to fetch-limited wind, time-limited pulse, in order to show the waveform effect on Doppler spectrum
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