PolSAR Image Feature Extraction via Co-Regularized Graph Embedding | |
Huang, Xiayuan; Nie, Xiangli; Qiao, Hong | |
刊名 | REMOTE SENSING |
2020-06-01 | |
卷号 | 12期号:11页码:19 |
关键词 | Wishart distance Euclidean distance of polarimetric features co-regularized graph embedding dimensionality reduction PolSAR image feature extraction |
DOI | 10.3390/rs12111738 |
英文摘要 | Dimensionality reduction (DR) methods based on graph embedding are widely used for feature extraction. For these methods, the weighted graph plays a vital role in the process of DR because it can characterize the data's structure information. Moreover, the similarity measurement is a crucial factor for constructing a weighted graph. Wishart distance of covariance matrices and Euclidean distance of polarimetric features are two important similarity measurements for polarimetric synthetic aperture radar (PolSAR) image classification. For obtaining a satisfactory PolSAR image classification performance, a co-regularized graph embedding (CRGE) method by combing the two distances is proposed for PolSAR image feature extraction in this paper. Firstly, two weighted graphs are constructed based on the two distances to represent the data's local structure information. Specifically, the neighbouring samples are sought in a local patch to decrease computation cost and use spatial information. Next the DR model is constructed based on the two weighted graphs and co-regularization. The co-regularization aims to minimize the dissimilarity of low-dimensional features corresponding to two weighted graphs. We employ two types of co-regularization and the corresponding algorithms are proposed. Ultimately, the obtained low-dimensional features are used for PolSAR image classification. Experiments are implemented on three PolSAR datasets and results show that the co-regularized graph embedding can enhance the performance of PolSAR image classification. |
资助项目 | National Key Research and Development Program of China[2017YFB1300200] ; National Key Research and Development Program of China[2017YFB1300203] ; National Natural Science Foundation of China[61802408] ; National Natural Science Foundation of China[91648205] ; National Natural Science Foundation of China[61627808] ; National Natural Science Foundation of China[91948303] ; National Natural Science Foundation of China[61806202] ; Strategic Priority Research Program of Chinese Academy of Science ; Fundamental Research Funds for the Central Universities[22120200149] |
WOS关键词 | UNSUPERVISED CLASSIFICATION ; DIMENSIONALITY REDUCTION ; SEGMENTATION |
WOS研究方向 | Remote Sensing |
语种 | 英语 |
出版者 | MDPI |
WOS记录号 | WOS:000543397000043 |
资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Science ; Fundamental Research Funds for the Central Universities |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/40001] |
专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组 |
通讯作者 | Huang, Xiayuan |
作者单位 | Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
推荐引用方式 GB/T 7714 | Huang, Xiayuan,Nie, Xiangli,Qiao, Hong. PolSAR Image Feature Extraction via Co-Regularized Graph Embedding[J]. REMOTE SENSING,2020,12(11):19. |
APA | Huang, Xiayuan,Nie, Xiangli,&Qiao, Hong.(2020).PolSAR Image Feature Extraction via Co-Regularized Graph Embedding.REMOTE SENSING,12(11),19. |
MLA | Huang, Xiayuan,et al."PolSAR Image Feature Extraction via Co-Regularized Graph Embedding".REMOTE SENSING 12.11(2020):19. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论