A New Segmentation Method of Synthetic Aperture Radar Image Based on Support Vector Machine

Image segmentation is a key step in the application of Synthetic Aperture Radar (SAR) images, but because of the existing of speckles in SAR images, image can not be divided well by using traditional methods. According to the remarkable result of wavelet transform on texture feature extraction, image filtration and the advantages of support vector machine (SVM) classification, an efficient method of SAR image segmentation is proposed. First, extracting texture feature of sample points by wavelet transform. Second, image preprocessing is performed by using wavelet filtering method. The vector composed of wavelet energy feature, weighted mean value of wavelet energy feature and gray values of eight-neighborhood of filtered SAR image. Then a SVM classifier is designed and trained by using normalized feature vectors. Finally, the testing sets of SAR images are divided by trained SVM. In the experiments of SAR image segmentation, better results have been obtained with this new method.