Polarimetric Scattering Interpretation And Three-Dimensional Reconstruction Of Man-Made Targets

With rapid development of radar imaging technology and polarimetric theory,polarimetric imaging radar system with both above-mentionded advantages has become the mainstream sensor in the fields of earth observation,reconnaissance,surveillance,remote sensing and so on.As an important observation object of polarimetric imaging radar system,man-made target has important research significance.Focusing on the scattering mechanism interpretation and three-dimensional(3D)reconstruction of manmade targets,this thesis aims at realizing the target scattering mechanism interpretation,structure recognition,geometric parameters inversion and 3D reconstruction.The main contents are as follows:Firstly,the fast general model-based decomposition is proposed.Considering the scattering mechanism interpretation ambiguity of man-made targets with large orientation angles,general model-based decomposition method is introduced and analyzed.Foucing on the high computation load of general model-based decomposition,a fast general model-based decomposition method is proposed.This research reveals the potential relationship between double-bounce orientation angles and polarimetric orientation angles.A derivation framework for double-bounce orientation angles is established in the form of piecewise linear function,and the refined double-bounce scattering model is reconstructed.The nonlinear optimization process is transformed into linear solution,and the rapid solution of angle parameters is realized.This research inherits the refined scattering models of general model-based decomposition and speeds up the parameter derivation procedure.Compared with state-of-art decomposition methods,the proposed method significantly improves the scattering mechanism interpretation performance of man-made targets,especially for buildings with large orientation angles.Secondly,the man-made target’ s scattering structure recognition methods based on Null-Pol theory are proposed.It’s the first time to apply the Null-Pol theory to man-made target’ s scattering structure recognition.A visiualization tool of Null-Pol response 3D patterns are constructed,based on which,a scattering structure recognition method of man-made targets is proposed.Considering the influence of scattering diversity,the NullPol response pattern interpretation tool is established by combining the polarimetric rotation domain method and Null-Pol theory.Then,a novel man-made structure recognition method is proposed based on selected features derived from the Null-Pol response patterns.Through the analysis of electromagnetic computation data of manmade targets such as vehicles and UAVs,the proposed Null-Pol scattering structure recognition methods can recognize more scattering types,such as vertical and horizontal dipole,left and right helix.What’s more,the recognition accurancy is 20% higher than that of Cameron decomposition on average.Under different polarimetric measurement errors,proposed methods achieve surperior and stable performance,compared with traditional Cameron decomposition.Thirdly,the man-made target geometric structure inversion and 3D reconstruction method is proposed.Considering the structure complexity of man-made target and difficulties in obtaining 3D information,the geometric structure inversion of man-made target is studied.According to the scattering structure recognition results,a geometric structure inversion method based on the spatial neighborhood characteristics of scattering center is proposed.The coordinates of scattering centers with same scattering types are projected onto the binary image,and the scattering centers are divided into different geometric structures with the assistance of morphological processing.Then,the 2D geometric parameters of structures can be obtained.Based on the imaging geometry,the height of vehicles can be derived,and a 3D reconstruction method is proposed.3D reconstruction experiments are performed on the electromagnetic simulation data of civilian vehicles.