文献类型：期刊文献

英文题名：One-class SVM based segmentation for SAR image

作者：Yan, Jianjun[1]; Zheng, Jianrong[1]

机构：[1] Center for Mechatronics Engineering, East China University of Science and Technology, Shanghai 200237, China

年份：2007

卷号：4493 LNCS

期号：PART 3

起止页码：959

外文期刊名：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

收录：EI(收录号：20080311036958)

语种：英文

外文关键词：Computer simulation - Human computer interaction - Numerical methods - Support vector machines

摘要：Image segmentation is of great importance in the field of image processing. A wide variety of approaches have been proposed for image segmentation. However, SAR image segmentation poses a difficult challenge owing to the high levels of speckle noise. In this paper, we proposed a SAR image segmentation method based on one-class support vector machines (SVM) to solve this problem. One-class SVM and two-class SVM for segmentation is discussed. One-class way is a kind of unsupervised learning, and one-class SVM based segmentation method reduces greatly human interactions, while yielding good segmentation results compared to two-class SVM based segmentation method. The segmentation results based on SVM are also compared to threshold method and adaptive threshold method. Experimental results demonstrate that the proposed method works well for image segmentation while reducing the speckle noise. ? Springer-Verlag Berlin Heidelberg 2007.