Research On Feature Extraction And Detection Of Man-made Target Using Polarimetric Sar Images

Polarimetric Synthetic Aperture Radar (PolSAR) identifies the fine configuration, orientation, geometric shape and composition of target using the SAR complex images in different polarimetric channels, and PolSAR represents wide applications in remote sensing. Feature extraction and target detection in SAR images using polarimetric information extraction technology are hot issues of PolSAR image interpretation and application with much theoretical and applicable significance. Based on the extraction of polarimetric information in SAR images, in order to improve the capability of image analysis and building target detection in PolSAR images, the polarimetric target decomposition, PolSAR image classification, target detection using PolSAR and PolInSAR images are studied systematically and detailedly in this dissertation.Firstly, the polarimetric characteristics of target and polarimetric target decomposition are deeply studied, including the coherence target decomposition, the incoherence decomposition based on eigenvalues and the incoherence target decomposition based on scattering model. Based on the theories and applications of existing decomposition methods, an extended Multiple-Component Scattering Model (MCSM) is proposed for PolSAR image decomposition, which considers single-bounce, double-bounce, volume, helix and wire scattering as elementary scattering mechanisms in the analysis of PolSAR images. The proposed MCSM is demonstrated with L-band full polarized images of DLR E-SAR of Oberpfaffenhofen test site in Germany and Danish EMISAR of Foulum test site in Denmark. The results validate that MCSM is effective for analysis of buildings in urban areas. Furthermore, the decomposition results can be used for further PolSAR classification and target detection.Secondly, PolSAR image classification is researched. Support Vector Machines (SVM) have good learning ability in case of small samples and structure risk minimization. The classification of polarimetric SAR image based on MCSM and SVM is presented in this paper. In order to take the scattering characteristics of itself and the spatial distribution into consideration, the decomposition results of MCSM and the texture features are combined in the SVM classifier. The validation experiment and performance evaluation are implemented using EMISAR PolSAR images. Compared with the classification result using Freeman and SVM, it can be found that the proposed classification method based on MCSM and SVM can obtain a good classification result and high precision. Subsequently, target detection based on granularity computing of quotient space theory using PolSAR images is proposed in this dissertation. The detection results of MCSM decomposition, polarimetric white filter, and polarimetric similarity parameter are considered as coarse granularity spaces. Then these three coarse granularity spaces are combined to construct the fine granularity space by using granularity synthesis algorithm based on quotient space theory. The fine granularity space is namely the optimal detection result. This method comprehensively utilizes the scattering characteristics, contrast and the similarity with typical target, and optimally combines the advantages of these three methods to realize high-precision detection. The target detection experiments based on MCSM, polarimetric similarity parameter, polarimetric white filter and their combination are also implemented using EMISAR data. The detection results demonstrate that the detection method based on granularity computing is better than a single detection algorithm. Compared the results based on granularity computing with the manual marks of buildings, it is found that the proposed detection method based on granularity computing is an effective target detection method in PolSAR images.Generally, buildings are distributed target in SAR image, and thus polarimetric similarity parameter based on Stokes matrix is presented, which is an extension of polarimetric similarity parameter based on Scattering matrix. Because targets present high coherence in PolInSAR image, polarimetric interferometric generalized eigenvalue similarity parameter is proposed. The target detection based on eigenvalues of PolInSAR coherence matrix and generalized eigenvalue similarity parameter is applied to E-SAR L band PolInSAR data and the results verify its effectiveness.