CASIA OpenIR  > 复杂系统管理与控制国家重点实验室
Supervised Polsar image classification by combining multiple features
Huang, Xiayuan1; Nie, Xiangli1; Qiao, Hong1; Zhang, Bo2
2019
Conference Name2019 IEEE International Conference on Image Processing (ICIP)
Conference Date2019/9/22-9/25
Conference PlaceTaipei, Taiwan
Abstract

For polarimetric synthetic aperture radar (PolSAR) image
classification, each pixel can be represented by multiple fea-
tures from different perspectives, such as polarimetric feature
(PF), texture feature (TF) and color feature (CF). Both multi-
view canonical correlation analysis (MCCA) and multi-view
spectral embedding (MSE) are two unsupervised multi-view
subspace learning methods which search for different pro-
jection matrices for different features to combine multiple
features in a common low-dimensional feature space. How-
ever, MCCA emphasizes the correlation of multiple features
and MSE learns the complementarity of multiple features.
To deeply learn the relation of multiple features, we incor-
porate MCCA with MSE based on the label information and
a symmetric version of revised Wishart (SRW) distance for
supervised PolSAR image feature extraction. Experimental
results confirm that the proposed method can improve the
classification performance.

Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26118
Collection复杂系统管理与控制国家重点实验室
复杂系统管理与控制国家重点实验室_机器人理论与应用
Corresponding AuthorHuang, Xiayuan
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.AMSS, Chinese Academy of Sciences
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,Nie, Xiangli,Qiao, Hong,et al. Supervised Polsar image classification by combining multiple features[C],2019.
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