Local Discriminant Canonical Correlation Analysis for Supervised PolSAR Image Classification
Huang, Xiayuan1; Zhang, Bo2,3; Qiao, Hong1,4; Nie, Xiangli1
Source PublicationIEEE GEOSCIENCE AND REMOTE SENSING LETTERS
2017-11-01
Volume14Issue:11Pages:2102-2106
SubtypeArticle
AbstractThis 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.
KeywordCanonical Correlation Analysis (Cca) Dimensionality Reduction (Dr) Local Discriminant Embedding (Lde) Multiview Feature Extraction Supervised Polarimetric Synthetic Aperture Radar (Polsar) Image Classification
WOS HeadingsScience & Technology ; Physical Sciences ; Technology
DOI10.1109/LGRS.2017.2752800
WOS KeywordPOLARIMETRIC SAR IMAGES ; FEATURE-EXTRACTION ; EFFICIENT ; FEATURES
Indexed BySCI
Language英语
Funding OrganizationBeijing Natural Science Foundation(4174107) ; National Natural Science Foundation of China(61379093 ; 61602483 ; 91648205)
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000413955500045
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19386
Collection复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
Corresponding AuthorNie, Xiangli
Affiliation1.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
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
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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