Classification Of Polarimetric SAR Images

Polarimetric synthetic aperture radar (SAR) is an advanced instrument for remote sensing. It obtains scattering characteristics of each resolution cell under different combinations of receiving and transmitting polarization, and records back scattering information of targets more completely than single-polarization SAR. It is helpful for analyzing target scattering characteristics. Classification of polarimetric SAR images is an important procedure of image interpretation. The classification map can be used as the middle result for edge extraction, target detection and recognition, etc., and also as the final result output directly to users. Investigation of classification of polarimetric SAR images is of much theoretical and applicable significance in the exploitation of target scattering characteristics, as well as the improvement of the application efficiency of polarimetric SAR systems.To reveal target scattering mechanisms and improving classification accuracy, classification methods of polarimetric SAR images are investigated in this thesis.It is very important to reveal target scattering mechanisms in analysis of polarimetric SAR data, nevertheless it is done generally for fully polarimetric data. Dual polarization is a frequently used operational mode of polarimetric SAR systems. In order to investigate performance of scattering mechanism identification of dual-polarization SARs, H-αdecomposition is modified. The boundary of the feasible region in H-αplane for dual-polarization cases is derived. Performance of dual-polarization SARs to distinguish three basic scattering mechanisms from an isotropic surface, a dipole, and an isotropic dihedral is studied theoretically. Performance of dual-polarization SARs to identify the eight scattering mechanisms inside the feasible region in H-αplane is analyzed, and a feasible division of H-αplane for HH-VV dual-polarization SAR is obtained in an experimental manner.Individual pixels are taken as elements in most classification methods for polarimetric SAR images so far. In order to improve classification accuracy, pixel-based classification methods are investigated by improving an existing method and introducing an algorithm developed in other fields. H-αdecomposition is one of the most famous unsupervised methods for classifying polarimetric SAR images. However, terrain classes are confused in the classification map of H-αdecomposition. Therefore the H-α-CM is proposed by integrating the C-mean algorithm, thus classification of scattering mechanisms is transformed into terrain classification. In this algorithm, to determine the number of iteration automatically, the entropy of a classification map is defined, and maximizing the entropy is taken as the termination criterion, which is demonstrated to be reasonable by experimental results. SVM is a new algorithm for classification and regression. It is used in classification of polarimetric SAR images herein. To avoid instability of classification performance induced by selecting features using experience, the NSVFS is proposed for feature selection, in which the number of support vectors is taken as the estimation index. Then it is used as a preprocess step in SVM classification, thus the NSSVM is constructed to select features and classify images using SVM. It is demonstrated by experiments that this algorithm is not very sensitive to SVM parameters, and has better self-adaptability. Finally, classification performance of full polarization versus dual and single polarization is compared qualitatively and quantitatively using SVM. Causes resulting in performance difference are illuminated by target scattering characteristics and operational mechanism of the classifier.Terrain details can be preserved in classification maps obtained by pixel-based methods. But performance of these methods is affected by the speckle. It is different for region-based methods. The spatial relation of neighboring pixels is considered in these methods, thus speckle effect is reduced effectively. MRF is a frequently used model for describing the spatial correlation between adjacent pixels. While introducing MRF into classification of polarimetric SAR images, in order to use completely the statistical a priori knowledge of the data and avoid information loss induced by separating the covariance matrix, the WMICM is proposed by integrating MRF and Wishart distribution of the covariance matrix. Then, aiming at the sawtooth effect of the watershed algorithm in over-segmentation, the MOS-ML is proposed using MRF for over-segmentation. The spatial relation of neighboring pixels is considered in different phase of the two algorithms. It is introduced in the second step of the WMICM, which contains initial classification and ICM adjustment, while introduced in the first step of the MOS-ML, containing initial over-segmentation and ML classification. Clear and smooth classification maps and high accuracy are observed using the two algorithms, due to full consideration of the statistical characteristic of polarimetric data and the spatial relation between adjacent pixels.