高分辨率极化SAR图像场景分类研究

项目名称： 高分辨率极化SAR图像场景分类研究

项目编号： No.61271401

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 无线电电子学、电信技术

项目作者： 杨文

作者单位： 武汉大学

项目金额： 75万元

中文摘要： 高分辨率极化SAR图像场景分类是SAR图像解译的前沿课题。本项目紧密结合高分辨率极化SAR传感器的发展与应用需求，开展高分辨率极化SAR图像场景分类算法研究。首先，研究基于统计和形态学表达的几何结构特征提取方法，以弥补传统极化特征的不足，为高分辨率极化SAR图像提供更有效的地物几何结构描述。其次，针对高分辨率极化SAR图像数据量大、标注像素级训练数据困难的情况，研究新的高分辨率极化SAR图像场景分类模型和算法。具体来说，一方面，结合隐语义模型和树结构分级随机场模型，利用仅有关键词标注的训练数据，研究弱监督分类算法；另一方面，在训练数据完全缺失的情况下，基于信息投影理论和聚类采样算法，研究能同时完成自动特征选择和自动确定地物类别数目的非监督分类算法。本项目研究可望在高分辨率极化SAR图像几何结构特征提取，场景分类模型与算法上取得显著进展，为高分辨率极化SAR图像自动解译和应用提供技术支撑。

中文关键词： 极化合成孔径雷达；特征提取；特征选择；分类；

英文摘要： High-resolution polarimetric SAR (PolSAR) image classification is a leading topic on the frontiers of interpreting SAR images. Compared to low-resolution ones, high resolution remote sensing provides a richer scene and target information (such as the geometric structure of the targets, etc.) for Earth observation, but at the same time raises new problems and challenges for the interpretation of images. This project focuses on the geometric structure feature extraction and scene classification of high-resolution PolSAR images, in order to meet the needs of the development of high-resolution PolSAR sensors and their applications. First, to address the inadequacies of traditional polarimetric scattering features for describing the high-resolution PolSAR images, a geometric feature extraction scheme will be studied based on statistical and morphologic representations, which shall provide an effective description of the geometric structures of images. Second,it is worth noticing that overwhelming quantities of high-resolution PolSAR data are now available and support accurate earth observations and topographic measurements, but traditional statistical learning methods require a large amount of densely-labeled training data to produce an effective terrain classifier. Such labeling is very labor-intensive and it typica

英文关键词： Polarimetric SAR；Feature Extraction；Feature Selection；Classification；