KMS Of Academy of mathematics and systems sciences, CAS
| Local Discriminant Canonical Correlation Analysis for Supervised PolSAR Image Classification | |
Huang, Xiayuan1; Zhang, Bo2,3 ; Qiao, Hong1,4; Nie, Xiangli1
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| 2017-11-01 | |
| Source Publication | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
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| ISSN | 1545-598X |
| Volume | 14Issue:11Pages:2102-2106 |
| Abstract | 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. |
| Keyword | 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 |
| Language | 英语 |
| Funding Project | Beijing Natural Science Foundation[4174107] ; National Natural Science Foundation of China[61379093] ; National Natural Science Foundation of China[61602483] ; National Natural Science Foundation of China[91648205] |
| WOS Research Area | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS Subject | Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS ID | WOS:000413955500045 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/26887 |
| Collection | 应用数学研究所 |
| Corresponding Author | Nie, Xiangli |
| Affiliation | 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 |
| Recommended Citation 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. |
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