摘要
在极化合成孔径雷达(synthetic aperture radar,SAR)图像理解和解译中,地物分类是重要的应用方向之一.为了研究多角度极化SAR图像的地物分类,文中基于极化统计特征差异性顺序,给出了多角度极化分解特征序列构建方法.首先,采用基于Wishart分布的统计量对非各向同性散射中心进行检测,并逐像素生成基于散射特征差异的新序列图像.然后,面向多种极化特征分解模型,提出通用的多角度极化特征一阶差分序列描述方法及编码方法,包括Yamaguchi四分量分解、Krogager分解以及H/A/Alpha分解,得到多维特征参数序列.最后,通过两种方法对比后最终选用支持向量机(support vector machine,SVM)方法对特征序列进行分类.通过机载P波段极化SAR开展360°观测试验,验证了该方法的有效性,并展示出在地物分类方面的应用潜力.
In the understanding and interpretation of polarimetric synthetic aperture radar(PolSAR)images,terrain classification is one of the most important applications.The paper studies the terrain classification of multi-angle polarimetric SAR images based on non-analytic scattering models.The feature model decomposes multi-angle polarimetric SAR images by three decomposition methods to obtain characteristic parameters,and finally classifies.First,we quantify and rank media polarimetric scattering dissimilarity over all aspects.In addition,for the multi-polarization feature decomposition model,a general multi-angle polarization feature first-order difference sequence description method and coding method are proposed,including Yamaguchi four-component decomposition,Krogager decomposition and H/A/Alpha decomposition,which are decomposed to obtain multi-dimensional characteristic parameters.Finally,the feature sequences are classified by support vector machines(SVM).The 360-degree observation experiment of Pband polarimetric SAR is carried out to verify the effectiveness of the method and reveal the application potential in the terrain classification of features.
作者
李洋
王官云
王彦平
林赟
洪文
LI Yang;Wang Guanyun;WANG Yanping;LIN Yun;HONG Wen(North China University of Technology,Beijing 100144,China;Institute of Electronics,Chinese Academy of Sciences,Beijing 100094,China)
出处
《电波科学学报》
EI
CSCD
北大核心
2019年第6期771-777,共7页
Chinese Journal of Radio Science
基金
国家重点研发计划(No.2018YFC1505103)
国家自然科学基金青年基金(No.61601007)
国家自然基金面上项目(No.61971456)
