Research On Knowledge-Aided Target Characteristics Analysis And Recogniton In SAR Imagery

As an effective sensor for acquiring information, synthetic aperture radar (SAR) is now widely used in military reconnaissance as well as geophysical remote sensing applications. Along with the increasing capacity of SAR collection, automated or semi-automated SAR target recognition, which allows quickly and efficiently identification of interested objects in SAR image, has become a crucial part of battlefield intelligence gathering and situation assessment. At present the principal challenge of SAR image target recognition is root in the variability of SAR signatures of targets and complexity of background environment and the limitied information in SAR observation is badly unsufficient in coping with this problem. That is to say, apart from SAR observation, other aided information such as expert knowledge should be introduced. Focusing on exploitation of expert knowledge for SAR target recognition, three key techniques such as knowledge representation, structural model description and knowledge application are studied in this dissertation.Starting from the basic meaning of knowledge, this paper firstly introduces the structural model knowledge and the contextural knowledge. The two types of knowledge respectively describe the internal structure of target and the causality between the target and the external environment. They should be very useful in SAR target recognition. Based on the structural model information of the frequency dependence and the aspect dependence in scattering center model, two kinds of structural model knowledge representation are briefly established. Aiming at the uncertainty in the experiential contextual knowledge, this paper discusses several kinds of representation and processing method of uncertain knowledge. The uncertainty representation of evidence and knowledge, uncertainty propagation and uncertainty combination of conclusions are subsequently elaborated.Secondly, in the aspect of structural model knowledge, according to the frequency dependence of scattering center in geometrical theory of diffraction (GTD) model, one dimensional structural model of high resolution radar target is studied, namely model parameter estimation method. After analyzing the validity of position parameter estimation of GTD model using subspace-type technique, an orthogonal projection-based method for parameter estimation is presented. According to weak coupling between scattering center position and type parameter in GTD model, this method firstly utilizes the shift-invariance characteristerics of signal subspace to estimate the position of scattering center, and then determine the type of scattering center in terms of the orthogonal projection of frequency-dependent term. The sequential estimation schema largely reduces computational complexity. Moreover theoristic analysis and simulated experiment illustrate that the method can simultaneously achieve accurate estimation and well bandwidth-generalized performance, which lays a foundation for the research of two dimensional structural model.Subsequently, based on the frenquency dependency and the aspect dependency in attributed scattering center model, two dimensional structural model of target in SAR image is studied. After reviewing the main flow of complex imagery-domain model parameters extraction, the effects of image segmentation is analyzed. Aiming at the above issue, we transform the scattering center model from the frequency-aspect domain into the image domain and analyze the distribution characteristics of attributed scattering centers fully, which provides a useful guidance for the segmentation of SAR image and type selection of attributed scattering centers. According to different aspect-dependence characteristics of localized and extended scattering centers in attributed scattering center model, a three steps of type selection, image segmentation and parameter estimation is proposed. Moreover, in order to improve the performance of parameter estimation, the inherent principles of lk norm regularization-based super-resolution in SAR complex image domain are studied, and a modified lk norm regularization method is presented. Owing to varying parameters, the proposed method can mitigate the problem of inconsistent resolution for scattering centers with different amplitudes. The above researches have completed the apllication basis of structural model knowledge.Finally, the application of the structural model knowledge and the contextual knowledge to SAR image target recognition is investigated. According to the hierarchy of the structural model knowledge, a sequential knowledge-aided processing flow is proposed. Therein the specific structural knowledge describes the local relation of stuctures, and its merit in SAR image target recognition is analyzed. Moreover, we employ the global structural knowledge to construct the main scattering structure and put forward an approximately translational and rotational invariant feature based attributed point matching algorithm for target recognition. On the other hand, the importance of the contextual knowledge in SAR image target recognition is stated. A model of contextual knowledge-aided target recognition is presented, and the confidence method as an application example is utilized to illustrate the detailed process.