Image and Signal Processing Methods

Dimensionality reduction method based on a tensor model

[+] Author Affiliations
Ronghua Yan

Northwestern Polytechnical University, School of Electronics and Information, Xi’an, China

Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, China

Jinye Peng

Northwestern Polytechnical University, School of Electronics and Information, Xi’an, China

Northwest University, School of Information and Technology, Xi’an, China

Dongmei Ma

Xi’an Janssen Pharmaceutical Ltd., Xi’an, China

Desheng Wen

Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, China

J. Appl. Remote Sens. 11(2), 025011 (May 31, 2017). doi:10.1117/1.JRS.11.025011
History: Received November 17, 2016; Accepted May 5, 2017
Text Size: A A A

Abstract.  Dimensionality reduction is a preprocessing step for hyperspectral image (HSI) classification. Principal component analysis reduces the spectral dimension and does not utilize the spatial information of an HSI. Both spatial and spectral information are used when an HSI is modeled as a tensor, that is, the noise in the spatial dimension is decreased and the dimension in a spectral dimension is reduced simultaneously. However, this model does not consider factors affecting the spectral signatures of ground objects. This means that further improving classification is very difficult. The authors propose that the spectral signatures of ground objects are the composite result of multiple factors, such as illumination, mixture, atmospheric scattering and radiation, and so on. In addition, these factors are very difficult to distinguish. Therefore, these factors are synthesized as within-class factors. Within-class factors, class factors, and pixels are selected to model a third-order tensor. Experimental results indicate that the classification accuracy of the new method is higher than that of the previous methods.

Figures in this Article
© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Ronghua Yan ; Jinye Peng ; Dongmei Ma and Desheng Wen
"Dimensionality reduction method based on a tensor model", J. Appl. Remote Sens. 11(2), 025011 (May 31, 2017). ; http://dx.doi.org/10.1117/1.JRS.11.025011


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

PubMed Articles
Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.