OSA's Digital Library

Applied Optics

Applied 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)

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



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

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

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)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. Z. Liu, J. Yan, D. Zhang, and Q.-L. Li, “Automated tongue segmentation in hyperspectral images for medicine,” Appl. Opt. 46, 8328–8334 (2007). [CrossRef]
  2. K. Gill, J. Ren, S. Marshall, S. Karthick, and J. Gilchrist, “Quality-assured fingerprint image enhancement and extraction using hyperspectral imaging,” in 4th International Conference on Imaging for Crime Detection and Prevention, London (2011).
  3. S. Sumriddetchkajorn and Y. Intaravanne, “Hyperspectral imaging-based credit card verifier structure with adaptive learning,” Appl. Opt. 47, 6594–6600 (2008). [CrossRef]
  4. T. Kelman, J. Ren, and S. Marshall, “Effective classification of Chinese tea samples in hyperspectral imaging,” Artific. Intell. Res. 2, 87–96 (2013).
  5. C. Zhao, X. Li, J. Ren, and S. Marshall, “Improved sparse representation using adaptive spatial support for effective target detection in hyperspectral imagery,” Int. J. Remote Sens. 34, 8669–8684 (2013). [CrossRef]
  6. S. E. Craig, S. E. Lohrenz, Z. Lee, K. L. Mahoney, G. J. Kirkpatrick, O. M. Schofield, and R. G. Steward, “Use of hyperspectral remote sensing reflectance for detection and assessment of the harmful alga, Karenia brevis,” Appl. Opt. 45, 5414–5425 (2006). [CrossRef]
  7. G. F. Hughes, “On the mean accuracy of statistical pattern recognition,” IEEE Trans. Inf. Theory 14, 55–63 (1968). [CrossRef]
  8. H. Abdi and L. J. Williams, Principal Component Analysis (WIREs Comp Stat, 2010).
  9. R. Dianat and S. Kasaei, “Dimension reduction of optical remote sensing images via minimum change rate deviation method,” IEEE Trans. Geosci. Remote Sens. 48, 198–206 (2010). [CrossRef]
  10. T. W. Du Bosq, J. M. Lopez-Alonso, and G. D. Boreman, “Millimeter wave imaging system for land mine detection,” Appl. Opt. 45, 5686–5692 (2006). [CrossRef]
  11. F. Ndi, F. Adar, and S. H. Atzeni, “Spectral imaging,” Readout 38, 68–73 (2011).
  12. F. Vagni, “Survey of hyperspectral and multispectral imaging technologies,” , 2007.
  13. R. Jošth, J. Antikainen, J. Havel, A. Herout, P. Zemčík, and M. Hauta-Kasari, “Real-time PCA calculation for spectral imaging (using SIMD and GP-GPU),” J. Real Time Image Proc. 7, 1–9 (2012).
  14. M.-Z. Wang, D.-M. Wang, W.-X. Xu, B.-Y. Chen, and K. Guo, “Parallel computing of covariance matrix and its application on hyperspectral data process,” in Geoscience and Remote Sensing Symposium (IGARSS), July22–27, 2012, pp. 4058–4061.
  15. R. O. Green, M. L. Eastwood, C. M. Sarture, T. G. Chrien, M. Aronsson, B. J. Chippendale, J. A. Faust, B. E. Pavri, C. J. Chovit, M. Solis, M. R. Olah, and O. Williams, “Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS),” Remote Sens. Environ. 65, 227–248 (1998). [CrossRef]
  16. S. Holzwarth, A. Müller, M. Habermeyer, R. Richter, A. Hausold, S. Thiemann, and P. Strohl, “HySens-DAIS 7915/ROSIS imaging spectrometers at DLR,” in Proceedings of the 3rd Earsel Workshop on Imaging Spectroscopy, Herrsching, Germany, May13–16, 2003, pp. 3–14.
  17. J. S. Pearlman, P. S. Barry, C. C. Segal, J. Shepanski, D. Beiso, and S. L. Carman, “Hyperion, a space-based imaging spectrometer,” IEEE Trans. Geosci. Remote Sens. 41, 1160–1173, (2003). [CrossRef]
  18. “Hyperspectral remote sensing scenes,” 2014, http://www.ehu.es/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes .
  19. C. C. Chang and C. J. Lin, “LIBSVM: a library for support vector machines,” ACM Trans. Intell. Syst. Technol. 2, 1–27 (2013).
  20. F. Melgani and L. Bruzzone, “Classification of hyperspectral remote sensing images with support vector machines,” IEEE Trans. Geosci. Remote Sens. 42, 1778–1790 (2004). [CrossRef]
  21. M. Rojas, I. Dópido, A. Plaza, and P. Gamba, “Comparison of support vector machine-based processing chains for hyperspectral image classification,” Proc. SPIE 7810, 78100B (2010). [CrossRef]
  22. J. Zabalza, J. Ren, Z. Wang, S. Marshall, and J. Wang, “Singular spectrum analysis for effective feature extraction in hyperspectral imaging,” IEEE Geosci. Remote Sens. Lett. 11, 1886–1890 (2014). [CrossRef]
  23. J. Zabalza, J. Ren, M. Yang, Y. Zhang, J. Wang, S. Marshall, and J. Han, “Novel folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing,” ISPRS J. Photogr. Remote Sens. 93, 112–122 (2014). [CrossRef]
  24. J. Ren, J. Zabalza, S. Marshall, and J. Zheng, “Effective feature extraction and data reduction with hyperspectral imaging in remote sensing,” IEEE Signal Process. Mag. 31(4), 149–154 (2014).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited