OSA's Digital Library

Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 53, Iss. 20 — Jul. 10, 2014
  • pp: 4440–4449

Structured covariance principal component analysis for real-time onsite feature extraction and dimensionality reduction in hyperspectral imaging

Jaime Zabalza, Jinchang Ren, Jie Ren, Zhe Liu, and Stephen Marshall  »View Author Affiliations


Applied Optics, Vol. 53, Issue 20, pp. 4440-4449 (2014)
http://dx.doi.org/10.1364/AO.53.004440


View Full Text Article

Enhanced HTML    Acrobat PDF (969 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

Presented in a three-dimensional structure called a hypercube, hyperspectral imaging suffers from a large volume of data and high computational cost for data analysis. To overcome such drawbacks, principal component analysis (PCA) has been widely applied for feature extraction and dimensionality reduction. However, a severe bottleneck is how to compute the PCA covariance matrix efficiently and avoid computational difficulties, especially when the spatial dimension of the hypercube is large. In this paper, structured covariance PCA (SC-PCA) is proposed for fast computation of the covariance matrix. In line with how spectral data is acquired in either the push-broom or tunable filter method, different implementation schemes of SC-PCA are presented. As the proposed SC-PCA can determine the covariance matrix from partial covariance matrices in parallel even without prior deduction of the mean vector, it facilitates real-time data analysis while the hypercube is acquired. This has significantly reduced the scale of required memory and also allows efficient onsite feature extraction and data reduction to benefit subsequent tasks in coding and compression, transmission, and analytics of hyperspectral data.

© 2014 Optical Society of America

OCIS Codes
(100.2960) Image processing : Image analysis
(100.4145) Image processing : Motion, hyperspectral image processing

ToC Category:
Image Processing

History
Original Manuscript: March 13, 2014
Revised Manuscript: May 27, 2014
Manuscript Accepted: May 28, 2014
Published: July 4, 2014

Citation
Jaime Zabalza, Jinchang Ren, Jie Ren, Zhe Liu, and Stephen Marshall, "Structured covariance principal component analysis for real-time onsite feature extraction and dimensionality reduction in hyperspectral imaging," Appl. Opt. 53, 4440-4449 (2014)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-53-20-4440

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Log in to access OSA Member Subscription

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Log in to access OSA Member Subscription

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Log in to access OSA Member Subscription

You do not have subscription access to this journal. Article level metrics are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Log in to access OSA Member Subscription

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited