A new Polarimetric Interferometry Inverse Synthetic Aperture Radar (Pol-InISAR) 3D imaging method for non-cooperative targets is proposed in this paper. 3D imaging of non-cooperative targets becomes possible by combining additional information of interferometric phase along with conventional 2D ISAR imaging. In the previously reported single-polarimetry InISAR based 3D imaging, only a single-channel based interferometric phase is available that can be exploited to reconstruct the 3D ISAR image. This limits the ability to obtain a full target's scattering response and therefore limits the estimation of an accurate interferometric phase. To overcome this constraint, full-polarimetry information is being exploited in this paper, which allows to select the optimal polarimetric combination through which the highest coherence can be obtained. A higher coherence leads to a reduction (optimally a minimization) of the phase estimation error. Consequently, with an optimal phase estimation, an accurate 3D imaging of the target is possible. To validate this proposed Pol-InISAR based 3D imaging approach, both simulated and real datasets are taken under consideration.

In this paper, we show the capabilities of a new maritime control system based on the processing of COSMO-SkyMed Synthetic Aperture Radar (SAR) images. This system aims at fast detection of ships that may be responsible for illegal oil dumping. In particular, a novel detection algorithm based on the joint use of the significance parameter, wavelet correlator and a two-dimensional Constant False Alarm Rate (2D-CFAR) is designed. Results show the effectiveness of such algorithms, which can be used by the maritime authorities to have a faster although still reliable response. The proposed algorithm, together with the short revisit time of the COSMO-SkyMed constellation, can help with tracking the scenario evolution from one acquisition to the next.

The Advanced Polarimetric Doppler Weather Radar Simulator (APDWRS) capable of generating I&Q time series voltages for a ground based fully-polarimetric Doppler weather radar is described in this paper. The Weather Research and Forecast (WRF) model is used in conjunction with the T-Matrix code to generate radar signals according to the propagation-modified covariance matrix. Mixtures of rain, hail, graupel and snow hydrometeors relative to a realistic weather phenomenon are considered. The polarimetric and Doppler observables estimated from the simulated radar signals showed a good agreement with those found in previous observations.

An algorithm based on a support vector machine (SVM) is proposed for hydrometeor classification. The training phase is driven by the output of a fuzzy logic hydrometeor classification algorithm, i.e., the most popular approach for hydrometer classification algorithms used for ground-based weather radar. The performance of SVM is evaluated by resorting to a weather scenario, generated by a weather model; the corresponding radar measurements are obtained by simulation and by comparing results of SVM classification with those obtained by a fuzzy logic classifier. Results based on the weather model and simulations show a higher accuracy of the SVM classification. Objective comparison of the two classifiers applied to real radar data shows that SVM classification maps are spatially more homogenous (textural indices, energy, and homogeneity increases by 21% and 12% respectively) and do not present non-classified data. The improvements found by SVM classifier, even though it is applied pixel-by-pixel, can be attributed to its ability to learn from the entire hyperspace of radar measurements and to the accurate training. The reliability of results and higher computing performance make SVM attractive for some challenging tasks such as its implementation in Decision Support Systems for helping pilots to make optimal decisions about changes inthe flight route caused by unexpected adverse weather.

A model is developed in this paper that simulates polarimetric radar returns from rain and hail at X-Band. To do this, the output data from an open-source meteorological model, namely the Weather Research and Forecast (WRF), and a numerical electromagnetic prediction code based on the Transmission-Matrix (T-Matrix) method are jointly used. Promising results are obtained that are in accordance with real observations. This model is intended to be used as input for a weather radar simulator with polarimetric and Doppler estimation capabilities.

In this paper the authors propose an innovative two-stage technique for ship detection which is applied to sea synthetic aperture radar (SAR) images. This technique is based on the joint use of Wavelet theory, in particular of the two-dimensional Discrete Wavelet Transform (2D-DWT), and the Constant False Alarm Rate (CFAR) processor. Real data acquired from COSMO-SkyMed (CSK) system have been processed to verify the effectiveness of this proposed new technique.