An SVM-based Method for Land and Sea Segmentation in Polarimetric SAR Images

摘要：

A Support Vector Machine (SVM) based method for land and sea segmentation in Polarimetric SAR (POLSAR) is proposed in this paper. The principle of SVM is first briefly summarized. Features that selected for SVM consist of 9 polarimetric features obtained from polarimetric target decompositions, i.e., Krogager, Freeman-Durden and Cloude decompositions, and 6 texture features calculated from first-order statistics. These 15 features are combined to feature vectors. The experiments are carried out on POLSAR data from Radarsat-2. The SVM classifier is obtained through training with selected land and sea samples and then applied in segmentation of the images to be tested. The segmentation results indicate the effectiveness of the proposed method. The results are analyzed and the parameter selection of SVM is discussed in brief.

关键词： polarimetric SAR land and sea segmentation support vector machine polarimetric decomposition texture

作者: Xuwu Su Hongshi Sang Guangyou Yang

作者单位: Institute for Pattern Recognition & Artificial Intelligence, Huazhong Univ. of Sci.& Tech. Wuhan 430 Institute for Pattern Recognition & Artificial Intelligence, Huazhong Univ. of Sci.& Tech. Wuhan 430 School of Mechanical Engineering, Hubei Univ. of Tech. Wuhan 430068,P.R.China

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 1219-1222

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）