Parametric SAR Imaging for Typical Lines and Surfaces

In the Synthetic Aperture Radar (SAR) remote sensing imagery of complicated scenes (especially urban scenes), there are a large number of lines and surfaces, such as roads in urban areas and the surfaces of buildings. These microwave-signal-scattering features have strong directivity. Traditional SAR acquires the scattering information of a scene from a single observation, and traditional imaging algorithms are based on the point target model, which makes the main features of the lines and surfaces in traditional SAR images appear as a series of strong scattering points rather than line-scattering and surface-scattering features. This outcome ultimately causes the target to be discontinuous in the SAR image, thus making the SAR image difficult to interpret. Therefore, in this study, we conducted an in-depth and meticulous investigation of the SAR imaging mechanism for lines and surfaces by establishing a parametric echo model of typical lines and triangular surfaces. Based on the proposed parametric model, we performed parametric imaging of these lines and surfaces. Based on our results, we propose a parametric imaging method, in which the typical lines and surfaces are classified and determined based on Bayesian theory and the proposed parametric model. Then, an SAR image can be obtained that effectively characterizes the scattering features of the line and surface targets by visual imaging, which effectively facilitates SAR image interpretation. The results of our numerical simulation experiments verify the validity of the proposed method.