Feature Parameter Estimation Of Sea Ships Based On SAR Imaging

Ship target identifiation is of great importance for the national coastline defenseand the shipping traffic management. With the fine resolution in all-day, all-weatherconditions, Synthetic Aperture Radar (SAR) plays an important role in the classificationand recognition of sea ships. To achieve this goal, a variety of ship features areextracted from SAR images. This dissertation, against the background of sea surfacereconnaissance and surveillance by using high resolution spaceborne/airborne SAR,focuses on the ship feature parameter estimation in the following four aspects.As regarding to the inadequacy in both quantity and quality of measured images,this dissertation uses simulated ones generated by the methods of electromagnetic (EM)modeling for most experiments, which are followed by some validations with measuredimages. For the purpose of self-containedness, some typical techniques of EM modelingand popular softwares are introduced. The composite models of the ships and the roughsurfaces used in this dissertation are then illustrated, as well as the hybrid method of EMmodeling. To testify the validity of the applied approach, the simulated radar crosssection and SAR images are provided and compared with those generated by the classicsoftware and the real measured data.Secondly, in order to suppress the disturbance of the noise and sea clutters in SARimages, the image pre-processing based on the Capon spectral estimation method isstudied. The Capon’s method is compared with other spectral estimation techniques.Subsenquently, its performances in improving the resolution of scattering centers andthe Target-to-Clutter Ratio (TCR) of SAR images are examined through the MonteCarlo simulations. Moreover, to solve the severe problem of memory comsuption, aniterative way to realize the two-dimensional (2D) Capon is put forward. Meanwhile, forthe SAR images with low TCR, a robust pose estimation algorithm is presented basedon the angle entropy of the Radon transform of the bi-valued images.Thirdly, to study the effect of multipath propagation on the ship image and featureparameter estimation under different sea conditions, a probability model is deduced forthe radar echo multipath delays of the scattering centers on a rough surface. In thismodel, the sea is considered as a rough surface satisfying the Kirchhoff approximationand described by a certain elevation-slope probability density function. Experimentally,we analyzed the relationship between the model and the factors such as the scatteringcenter height, the radar elevation angle, and the sea characteristic parameters. Themodel is then used in the feature parameter estimation in both2D and three-dimensional(3D) problems, with different modifications for different sea conditions. In the end, to eliminate the uncertainty in the2D geometrical features, such as thelength, width, and pose angle, an outline-based method is proposed to estimate the3Dfeatures for ship images with unfocused scattering centers. The method utilizes morethan one image with known radar elevation angles and extracts the feature ellipses ofthe ship outlines. By reconstructing the objective ellipsoid of these ellipses, one canfinally get the3D geometrical features of the ship, including the size in each dimensionand the radar azimuth while imaging. The performance of the algorithm is analyzedwith Monte Carlo simulations. Moreover, feature estimation and target classificationexperiments are carried out with simulated images of several kinds of ships, whichtestify the effectiveness of the algorithm.