Segmentation Of Synthetic Aperture Radar Image Based On Support Vector Machine

Image segmentation is a key step in the application of synthetic aperture radar (SAR) image. However, because of the existing of speckles and unsuitable feature extraction, SAR image can not be segmentalized well by using traditional methods. So it is important to apply the new research of machine learning theory to SAR image segmentation and construct the effective classifier.The 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 speckle noise in SAR image are studied. This provides theoretical basis for image processing.Secondly, 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 machine is proposed. The procedures of the method is as follow: First, texture feature of sample points is extracted by wavelet transform method. Second, image preprocessing is performed by using wavelet filtering method. Third, the comprehensive 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-neighborhood. Fourth, a SVM classifier is designed and trained by using normalized feature vectors. At last, the testing sets of SAR image are sorted 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 single-target SAR image segmentation.Finally, a new multi-target SAR image segmentation method based on support vector machine is proposed. Samples obtaining is processed by artificial choice, then comprehensive characteristics is regarded as characteristic vector to train support vector machine, this obtained SVM is used to realize multi-target SAR image segmentation. Among them,"one-against-one method"is adopted in the process of two kinds classification extend to various kinds classification. Eventually, through a multi-target SAR image segmentation experiment, the proposed method outperforms the previous traditional algorithm.