Multi-View Feature Selection for PolSAR Image Classification via l(2,1) Sparsity Regularization and Manifold Regularization
文献类型：期刊论文
| 作者 | Huang, Xiayuan ; Nie, Xiangli
|
| 刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING
 |
| 出版日期 | 2021 |
| 卷号 | 30页码:8607-8618 |
| 关键词 | Multi-view feature selection PolSAR image classification l(2,1) norm manifold regularization |
| ISSN号 | 1057-7149 |
| DOI | 10.1109/TIP.2021.3118976 |
| 通讯作者 | Huang, Xiayuan(xiayuan.huang@ia.ac.cn) |
| 英文摘要 | Feature is a crucial element of polarimetric synthetic aperture radar (PolSAR) image classification. Multiple types of Features, such as polarimetric features (PF) generated from the PolSAR data and various polarimetric target decompositions, texture features (TF) of the Pauli color-coded PolSAR images are used as features for PolSAR image classification. The obtained PF and TF often form the high-dimensional data, which leads to high computational complexity. Moreover, some features are irrelative and do nothing to improve the classification performance. Therefore, it is fairly indispensable to select a subset of useful features for PolSAR image classification. This paper proposes a multi-view feature selection method for PolSAR image classification. Firstly, two types of features, PF and TF are generated separately. Then the optimization model is built to pursue the feature selection matrices. Specifically, in order to maintain the consistency of different types of features, we search for the common representation of multiple types of features in the optimization problem. The l(2,1) norm sparsity regularization is imposed on the feature selection matrices to achieve feature selection. In addition, the manifold regularization on the common representation is utilized to preserve the structure information of the data. The effectiveness of the proposed method is evaluated on three real PolSAR data sets. Experimental results demonstrate the superiority of the proposed method. |
| WOS关键词 | SAR ; SEGMENTATION ; ENTROPY |
| 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[62076241] ; National Natural Science Foundation of China[91948303] ; National Natural Science Foundation of China[61806202] ; National Natural Science Foundation of China[91648205] ; National Natural Science Foundation of China[61627808] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000709070800002 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/46194]  |
| 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. Multi-View Feature Selection for PolSAR Image Classification via l(2,1) Sparsity Regularization and Manifold Regularization[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2021,30:8607-8618. |
| APA | Huang, Xiayuan,&Nie, Xiangli.(2021).Multi-View Feature Selection for PolSAR Image Classification via l(2,1) Sparsity Regularization and Manifold Regularization.IEEE TRANSACTIONS ON IMAGE PROCESSING,30,8607-8618. |
| MLA | Huang, Xiayuan,et al."Multi-View Feature Selection for PolSAR Image Classification via l(2,1) Sparsity Regularization and Manifold Regularization".IEEE TRANSACTIONS ON IMAGE PROCESSING 30(2021):8607-8618. |
入库方式： OAI收割
来源：自动化研究所
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