Study On Classification Methods Of Polarimetric SAR Images

SAR image classification is an essential integral part of SAR image processing and also a key technique for SAR image interpretation. Fast and accurate SAR image classification is a critical pre-processing step in various practical applications. Nowadays countries like USA, Canada, Germany and Japan are playing the leading role in SAR image classification, while China, owing to its late start, is still in the elementary phase of development in this field. But for all of them, leading or following, the performance of the SAR image classification need be improved and the efficiency and robustness of the classification algorithms be enhanced. By now, the field of SAR image classification methods is still a hotspot for intensive and original research.. This dissertation is devoted to the exploration and development of new methods of classification based on Polarimetric SAR (PolSAR) images.Polarimetric synthetic aperture radar is a new type of imagery radar which is used to measure the polarization feature of the scattering signal from the targets. It has the advantage of getting SAR images of multiple polarization channels and it is helpful in understanding and defining scattering mechanism, and improving radar performance in target detection, discrimination and classification. Furthermore, it is also superior in clutter suppression and anti-jamming to those which do not acquire polarimetric information. PolSAR expands the application area of the SAR system and becomes more and more important in applications to collecting information of physical and electromagnetic architecture of superstratum on the ground.Full polarization SAR data give not only the amplitude and phase information of each polarization channel, but also the relative phase information between each two channels. So, PolSAR data provide more target information on the ground than ordinary SARs do. With the additional polarimetric information, better SAR image processing results can be obtained. The existing PolSAR image classification methods have not dug the polarimetric information in depth. In fact, Extracting and utilizing the polarimetric information adequately would enable us to define and interpret scattering mechanisms better, and then better classification results can be obtained. Furthermore, data processing techniques are in fast progress and some new ones such as independent component analysis (ICA) are emerging continuously in recent years. The classification results may be improved further if these techniques are integrated into the applications of SAR image classification.In addition to utilizing the polarimetric information adequately and introducing ICA into PolSAR image classification, this dissertation applies subaperture analysis, which is a new SAR image processing technique, to statistical analysis of polarimetric scattering. Making use of the analysis results in PolSAR image classification, the classification effects are improved.The main work and contributions accomplished in this dissertation are as follows:(1) The fundamental theory of electromagnetic wave polarimetry in radar, including the polarization representation of EM wave, the scattering coordinate system in polarimetric SAR imaging and the polarimetric scattering description, is presented. And some polarization problems under Jones vector's complex scalar representation are derived.(2) The existing classification algorithms for PolSAR images, such as Pauli decomposition, Krogager docomposition, H/αclassification, H/A/αclassification, Wishart classification and fuzzy c -means classification are analytically and experimentally studied. The Krogager decomposition under scattering power matrix in HV linear polarization basis is derived.(3) The basic theory, model, constraints and optimized algorithms of ICA are introduced.(4) ICA based PolSAR image classification methods are studied in this dissertation. The classification method associated with ICA based speckle reduction and the classification method directly using ICA are presented. In classification associated with ICA based speckle reduction, the speckle suppression process is carried out on the color channels after decomposition rather than the polarization channels. This solves the problem of relative phase missing and improves the classification results. In classification based on ICA, the desirable independent components and the speckle are separated simultaneously, so speckle reduction and classification become a unified process. And by using this method, the classification accuracy is enhanced.(5) The subaperture analysis of PolSAR images is studied. Firstly the nonstationary target detection based on subaperture analysis is investigated and two different methods are proposed to detect different kinds of nonstationary targets. Secondly the nonstationary target detection results are combined with H/αclassification and the stationarity- H/αclassification method is presented. With this method, the classification fineness is enhanced. Then the unsupervised Wishart classification algorithm based on subaperture data is studied and the subaperture-H/α/Wishart classification method is proposed. This method improves the classification effects of unsupervised Wishart classification.(6) Finally, several conclusions of the dissertation are drawn and the research subjects in the future are proposed.