基于支持向量机合成孔径雷达图像分割.pdf

ABSTRACTImage segmentation is a key step in the application of synthetic aperture radar (SAR) , because of the existing of speckles and unsuitable feature extraction, SAR image can not be segmentalized well by using traditional it is important to applythe new research of machine learning theory to SAR image segmentation and constructthe effective method of SAR image feature extraction and segmentation, which is based on support vector machine(SVM), is researched deeply in this thesis. The main contents and contributions are as follows:Firstly, the study background, significance, research status and development trend of SAR image segmentation are introduced. And the statistical learning theory, support vector machines, synthetic aperture radar and specklenoise in SAR image are studied. This provides theoretical basis for image , according to the remarkable results of wavelet transform on texture feature extraction and image filtration as well as the advantages of SVM classification, a new single-target SAR image segmentation method based on support vector machineis procedures of the method is as follow: First, texture feature of sample pointsis extracted by wavelet transform , image preprocessing is performed by using wavelet filtering , prehensive feature of sample points is constructed by wavelet energy features, weighted mean value of wavelet energy features,the gray values of the sample points which is denoising, and the gray values of eight-, a SVM classifier is designed and trained by using normalized feature last, the testing sets of SAR image aresorted by trained SVM so that the single-target SAR image can be segmentalized. With the experiment result, the method is proved an efficient one of Subject:Se