Three-Dimensional Polarimetric InISAR Imaging of Non-Cooperative Targets

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

A support vector machine hydrometeor classification algorithm for dual-polarization radar

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 in the flight route caused by unexpected adverse weather

A Validation Procedure for a Polarimetric Weather Radar Signal Simulator

A simulator of weather radar signals can be exploited as a useful reference for many applications, such as weather forecasting and nowcasting models or for training artificial intelligence systems designed to optimize the trajectory of aircrafts with the purpose to reduce flight hazard and fuel consumption. However, before being used, it must be accurately examined under different operating conditions, in order to evaluate the consistency of the outputs produced. In this paper, we present a validation procedure for a newly developed polarimetric weather radar simulator (POWERS). The goal is to assess the ability of the simulator to deal with any kind of input data, be they simulated and real raindrop-size distributions, or outputs generated by numerical weather prediction models. Three different approaches are proposed, each providing a connection between meteorological inputs and the radar observables simulated by POWERS. The analysis is carried out in the case of rainfall, both at S- and X-bands

Polarimetric Radar Signal Processing Techniques based on Decomposition Theorems for Detection and Classification of Natural and Man-made Targets

The field of polarimetry has as its object to study the state of polarization of an electromagnetic (e.m.) wave and its changes of state. Radar polarimetry is based on the concept that the knowledge of the behavior of a wave interacting with a target, allows the radar to use not only the whole power re-radiated by the target, but also information not present in traditional radar. In other words, a wave which travels in free space brings with it a large number of information (amplitude, Doppler frequency, phase, direction), among which the polarization, that is, the trajectory described by the electric field vector. When the wave comes into contact with a target, it is re-radiated and received by the radar with a state of polarization, which may be different from the one used in transmission. In fact, such a change or transformation is related not only to the polarization used in transmission, but also to the geometric and structural characteristics of the object. The main limitation of the radar used in the 70'-80' was due to the fact that they transmitted and received with the same polarization. This means that, using a mono-polarized radar, a change of state of polarization, due to the presence of a target, is not disclosed by the radar itself, causing a considerable loss of information. This suggests that full polarimetric survey and subsequent extraction of information are a valuable aid for the detection and classification of the target. Indeed, it is thanks to the growth of the theory of radar polarimetry and the phenomenological theory of the target that the design technology of complex equipment has been able to grow up to the design of fully polarimetric radar that makes use of discriminant of polarization states. The benefits of an e.m. wave polarimetric study are related not only to the detection, classification and recognition of targets, but also to a minimization of the effects of multipath in the radar mapping and tracking in the field of remote sensing and imaging applications. In this thesis we focus mainly on the applications of radar polarimetry in various fields, and showing the feasibility of new methods of feature extraction, namely scattering matrix decomposition method in support of more traditional polarimetric feature extraction for improved classification for various types of target. First part of thesis deals with volumetric target simulation and analysis, namely meteorological targets, both airborne and ground-based, and introduces the concept of Polarimetric Target Decomposition, showing how polarimetry, especially in the case of airborne operations, is able to help to detect and classify possible hazards encountered during the flight, or, in the case of ground-based operation, is able to give a enhanced awareness of the structure of the perturbation with a better understanding of the whole picture. In current avionic systems, for example, is impossible to distinguish the type of precipitation, water, snow, hail. Of course, assumptions can be done, i.e., high reflectivity in a zone where temperature is 15-20 degrees below zero is likely to indicate an hailstorm, but we can have no precise information on type of precipitation near and below the melting height (which also depends on season and geographic region). About 70% of the high-reflectivity echoes that pilots see on their radar are non-hazardous (other than causing a decrease in visibility and making runways wet). To determine whether or not a particular “red” echo is hazardous in terms of turbulence and hail and other dangers, the pilot must first know if the atmosphere in which he is flying is conducive to hail and high turbulence. It is worth noting to recall that heavy rain without turbulence is not an issue for the safety of the flight. But even with atmospheric knowledge, a pilot cannot say whether a particular high-reflectivity area is hazardous. Usually, the pilot evades that area, with an increase of costs, time and polluting emissions due to the detour. In the latter part we describe also the work made in collaboration with TU Delft in the Netherlands using real polarimetric radar data. The second part of the thesis deals with man-made target polarimetric analysis; we analyzed in particular a marine environment, studying a system of detection of ships responsible of illegal discharges in the sea. While first part of the system deals with the analysis of non-polarimetric data, spill and ship detection, the second part is oriented to the feasibility of polarimetric classification of different ships

Ship detection from SAR images based on CFAR and wavelet transform

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

Fast detection of oil spills and ships using SAR images

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

Detection of ships from SAR images

The observation of maritime activity has been a field of research ever since SAR images of the ocean surface became available. This is because SAR images provide global scale coverage, independently of the weather and of the night/day cycle, and high spatial resolutions. More specifically, ship detection and classification from SAR images are effective tools for fishing activity monitoring and detection of ships responsible for marine oil pollution. Furthermore, SAR images are used for detecting illegally operating ships that increase marine crimes including smuggling and sea-jacking by piracy