基于多特征多核学习的全极化SAR溢油提取方法  

Full polarimetric SAR oil-spill extraction method based on multi-feature and multi-kernel learning

作　　者：刘善伟 张世豪 李翔宇[2] 张乃心 张婷[3] LIU Shan-wei;ZHANG Shi-hao;LI Xiang-yu;ZHANG Nai-xin;ZHANG Ting(China University of Pertroleum,Qingdao 266580,China;Dalian Environmental Monitoring Centre,Dalian 116023,China;First Institute of Oceanogaphy,State Oceanic Administration,Qingdao 266061,China)

机构地区：[1]中国石油海洋研究所大学(华东),山东青岛266580 [2]大连市环境监测中心,辽宁大连116023 [3]国家海洋局第一海洋研究所,山东青岛266061

出　　处：《海洋科学》2018年第1期112-118,共7页Marine Sciences

基　　金：国家重点研发计划项目(2017YFC1405600);国家自然科学基金(41706208,41776182);山东省自然科学基金(ZR2016DM16)

摘　　要：全极化合成孔径雷达(Synthetic Aperture Radar,SAR)数据具有丰富的极化信息,能够提取出大量异构性特征。核学习方法在解决小样本、高维特征分类问题上具有优势,但异构特征对不同核函数具有响应差异。本文利用一种引入先验标签的多核学习方法进行全极化SAR的溢油信息提取,即基于分析结果对特征集进行遴选与组合,分别在每个特征组合中训练得到一个预备层核函数,以新获取的预备层核函数作为新的底层核函数,对全部特征进行学习分类。通过提取与分析溢油和海水的统计特征、物理散射特征和纹理特征,建立溢油全极化SAR特征谱,并利用引入先验标签的多核学习分类器进行溢油提取实验。结果表明,该方法能够利用全极化SAR多维异构特征的互补特性有效提高溢油分类提取精度。Full polarimetric SAR data contains a wealth of polarization information,so a large number of beterogeneous features can bge extracted from it.It kernel learning method has advantages in solving small-sample problems and high-dimensional feature classification,but heterogeneous feature sets based on analysis results and trains a preliminary kernel function on each function to classify all the features The full polarimetric cal-scattering,and texture features of the oil spill and seawater.We conducted an oil-spill extraction experiment using the above multi-kernel learning classifier,The results show that this method can effectively improve the precision of oil-spill classification by using the complementary characteristics of multi-dimensional heterogeneous features of full polarimetric SARdata.

关 键 词：全极化SAR(Synthetic APERTURE Radar) 溢油提取 多核学习 

分 类 号：P76[天文地球—海洋科学]