基于张量稀疏L1图的半监督极化SAR影像地物分类

项目名称： 基于张量稀疏L1图的半监督极化SAR影像地物分类

项目编号： No.61502369

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 其他

项目作者： 刘红英

作者单位： 西安电子科技大学

项目金额： 21万元

中文摘要： 项目针对多极化合成孔径雷达(SAR)影像的海量、高维、多通道与多模态数据,难以准确高效的自动解译的问题，探索基于图模型的半监督地物分类方法，旨在降低标注样本数目的同时提高分类精度。首先，针对当前极化SAR地物分类中多物理属性描述与特征选择的困难，研究极化SAR数据多特征的张量表示与稀疏建模；其次，设计张量稀疏表示的流形正则，构造基于张量稀疏L1图的鲁棒半监督分类算法; 在此基础上, 挖掘极化SAR数据的空域结构，设计数据域-空域联合的流形约束，发展基于双流形正则的半监督分类算法。最后，研究基于高分辨率极化SAR地物分类的快速实现方法，用RADARSAT-2、PALSAR等数据验证算法的有效性。项目不仅能提高现有极化SAR影像数据自动解译的性能，还将进一步完善和推广半监督方法的研究和应用。研究成果可应用于军事、民用等相关领域，为有效支撑雷达系统对地观测、卫星导航、深空探测等国家需求奠定基础。

中文关键词： 多极化合成孔径雷达；半监督学习；张量；稀疏编码；流形正则

英文摘要： This project addresses the challenge that multi-polarimetric SAR data brings about the large scale, high dimensional, multi-channel and multi-mode for the inaccurate and inefficient auto-interpretation. And this project explores the semi-supervised terrain classification using graph models aiming at higher classification accuracy at a smaller number of labeled samples. Firstly, to overcome the difficulty of describing the multi-attributes of the data and selecting features, the tensor representation and sparse modeling for polarimetric SAR are investigated. Secondly, designs the manifold regularization by the coefficients from sparse representation, constructs the sparse-representation model for polarimetric SAR data, and builds a noise-robust semi-supervised classification algorithm. Based on it, this project investigates the spatial information of polarimetric SAR data, designs the spatial manifold regularization, and develops the semi-supervised classification algorithms by means of double manifold regularizations. Finally, this project studies the fast realization of classification algorithms for high-resolution polarimetric SAR. All the above algorithms are verified on the RADARSAT2 and PALSAR datasets. This project will not only improve the performance of polarimetric SAR processing and interpretation but also contributes to the improvement and extension of semi-supervised learning. The research could be applied to both the military and civilian, and paves good ways for the earth observation, satellite navigation, and space exploration of our nation.

英文关键词： Polarimetric SAR;semi-supervised learning;tensor;sparse coding;manifold regularization