Study On POL-SAR Image Classification Based On Target Decomposition And SVM

POL-SAR is a new kind of radar. Its imaging technique is developed from SAR. Carried forward the merit of SAR, POL-SAR can create high resolution images. Because of the wide and latent applications of POL-SAR in many areas of military, civil and scientific researches, POL-SAR is one of the most active fields in Radar and Remote Sensing nowadays.In this dissertation,the classification of POL-SAR image is studied. Main aspects include scattering feature extraction of POL-SAR image base on target decomposition, texture feature extraction based on gray-level o-occurrence matrix and POL-SAR image classification based on SVM.This dissertation is organized as follows, firstly, an overview of the domain of POL-SAR is provided. Then the development and achievement of POL-SAR decomposition and POL-SAR image classification is provided in detail. SVMand its application in different areas are introduced, followed by an introduction of the basic theory of SVMin chapter 2.Next, the polarimetric scattering characteristics of the target, including polarimetric scattering matrix, covariance matrix, coherence matrix and several polarimetric scattering mechanisms, are introduced. POL-SAR image feature extraction is deep discussed combined with examples. On this basis, a set of feature extraction methods are presented, Corresponding experiments are done using ESAR L-band data. The results are also provided and detailedly analyzed.The last step, feature selection and POL-SAR image classification based on SVM is mainly studied. Parameter selection of SVMis studied. The relationship of different parameters is compared. Then, classification arithmetic is proposed and used for POL-SAR image classification. Another classification experiments are also done. The method we proposed is validated through comparing with the results of other classification methods.