Local Discriminant Canonical Correlation Analysis for Supervised PolSAR Image Classification
文献类型：期刊论文
| 作者 | Huang, Xiayuan1 ; Zhang, Bo2,3; Qiao, Hong1,4; Nie, Xiangli1
|
| 刊名 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
 |
| 出版日期 | 2017-11-01 |
| 卷号 | 14期号:11页码:2102-2106 |
| 关键词 | Canonical Correlation Analysis (Cca) Dimensionality Reduction (Dr) Local Discriminant Embedding (Lde) Multiview Feature Extraction Supervised Polarimetric Synthetic Aperture Radar (Polsar) Image Classification |
| DOI | 10.1109/LGRS.2017.2752800 |
| 文献子类 | Article |
| 英文摘要 | This letter proposes a novel multiview feature extraction method for supervised polarimetric synthetic aperture radar (PolSAR) image classification. PolSAR images can be characterized by multiview feature sets, such as polarimetric features and textural features. Canonical correlation analysis (CCA) is a well-known dimensionality reduction (DR) method to extract valuable information from multiview feature sets. However, it cannot exploit the discriminative information, which influences its performance of classification. Local discriminant embedding (LDE) is a supervised DR method, which can preserve the discriminative information and the local structure of the data well. However, it is a single-view learning method, which does not consider the relation between multiple view feature sets. Therefore, we propose local discriminant CCA by incorporating the idea of LDE into CCA. Specific to PolSAR images, a symmetric version of revised Wishart distance is used to construct the between-class and within-class neighboring graphs. Then, by maximizing the correlation of neighboring samples from the same class and minimizing the correlation of neighboring samples from different classes, we find two projection matrices to achieve feature extraction. Experimental results on the real PolSAR data sets demonstrate the effectiveness of the proposed method. |
| WOS关键词 | POLARIMETRIC SAR IMAGES ; FEATURE-EXTRACTION ; EFFICIENT ; FEATURES |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000413955500045 |
| 资助机构 | Beijing Natural Science Foundation(4174107) ; National Natural Science Foundation of China(61379093 ; 61602483 ; 91648205) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/19386]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心 |
| 通讯作者 | Nie, Xiangli |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, State Key Lab Sci & Engn Comp, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Inst Appl Math, Beijing 100190, Peoples R China 4.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Huang, Xiayuan,Zhang, Bo,Qiao, Hong,et al. Local Discriminant Canonical Correlation Analysis for Supervised PolSAR Image Classification[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2017,14(11):2102-2106. |
| APA | Huang, Xiayuan,Zhang, Bo,Qiao, Hong,&Nie, Xiangli.(2017).Local Discriminant Canonical Correlation Analysis for Supervised PolSAR Image Classification.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,14(11),2102-2106. |
| MLA | Huang, Xiayuan,et al."Local Discriminant Canonical Correlation Analysis for Supervised PolSAR Image Classification".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 14.11(2017):2102-2106. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
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。