Local Discriminant Canonical Correlation Analysis for Supervised PolSAR Image Classification
Huang, Xiayuan1; Zhang, Bo2,3; Qiao, Hong1,4; Nie, Xiangli1
发表期刊IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
2017-11-01
卷号14期号:11页码:2102-2106
文章类型Article
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.
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
WOS标题词Science & Technology ; Physical Sciences ; Technology
DOI10.1109/LGRS.2017.2752800
关键词[WOS]POLARIMETRIC SAR IMAGES ; FEATURE-EXTRACTION ; EFFICIENT ; FEATURES
收录类别SCI
语种英语
项目资助者Beijing Natural Science Foundation(4174107) ; National Natural Science Foundation of China(61379093 ; 61602483 ; 91648205)
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号WOS:000413955500045
引用统计
被引频次:3[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/19386
专题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
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
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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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