A Novel Tensor-Based Feature Extraction Method for Polsar Image Classification
Huang, Xiayuan1; Nie, Xiangli1; Qiao, Hong1; Zhang, Bo2
2019-11-14
Conference NameIGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
Conference Date28 July-2 Aug. 2019
Conference PlaceYokohama, Japan, Japan
Abstract

Spatial information helps improve the performance of polarimetric synthetic aperture radar (PolSAR) image classification. Some existing methods have combined the spatial information and polarimetric features by the third-order tensor representation for feature extraction. They describe a pixel with the patch centered on this pixel. But they neglect the spatial heterogeneity, which may influence the classification performance. Therefore, we firstly seek k nearest samples based on the polarimetric feature similarity for each pixel to construct the second-order tensor, whose first order denotes the nearest samples and the second order denotes the polarimetric features. Moreover, k nearest samples are searched in a spatial local region rather than the full image, which can exploit the spatial information and reduce the computational burden. Then we employ tensor principal component analysis (TPCA) to extract low-dimensional features. Experimental results demonstrate that the proposed method can improve the classification performance compared with other methods.

Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/40602
Collection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. A Novel Tensor-Based Feature Extraction Method for Polsar Image Classification[C],2019.
Files in This Item: Download All
File Name/Size DocType Version Access License
IGARSS2019.pdf(363KB)会议论文 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Huang, Xiayuan]'s Articles
[Nie, Xiangli]'s Articles
[Qiao, Hong]'s Articles
Baidu academic
Similar articles in Baidu academic
[Huang, Xiayuan]'s Articles
[Nie, Xiangli]'s Articles
[Qiao, Hong]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Huang, Xiayuan]'s Articles
[Nie, Xiangli]'s Articles
[Qiao, Hong]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: IGARSS2019.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.