OSA's Digital Library

Applied Optics

Applied Optics


  • Editor: Joseph N. Mait
  • Vol. 52, Iss. 6 — Feb. 20, 2013
  • pp: 1330–1338

Separability between pedestrians in hyperspectral imagery

Jared Herweg, John Kerekes, and Michael Eismann  »View Author Affiliations

Applied Optics, Vol. 52, Issue 6, pp. 1330-1338 (2013)

View Full Text Article

Enhanced HTML    Acrobat PDF (1012 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



The popularity of hyperspectral imaging (HSI) in remote sensing continues to lead to it being adapted in novel ways to overcome challenging imaging problems. This paper reports on research efforts exploring the phenomenology of using HSI as an aid in detecting and tracking human pedestrians. An assessment of the likelihood of distinguishing between pedestrians based on the measured spectral reflectance of observable materials and the presence of noise is presented. The assessments included looking at the spectral separation between pedestrian material subregions using different spectral-reflectance regions within the full range (450–2500 nm), as well as when the spectral content of the pedestrian subregions are combined. In addition to the pedestrian spectral-reflectance data analysis, the separability of pedestrian subregions in remotely sensed hyperspectral images was assessed using a unique data set garnered as part of this work. Results indicated that skin was the least distinguishable material between pedestrians using the spectral Euclidean distance metric. The clothing, especially the shirt, offered the most salient feature for distinguishing the pedestrian. Additionally, significant spectral separability performance is realized when combining the reflectance information of two or more subregions.

© 2013 Optical Society of America

OCIS Codes
(100.3008) Image processing : Image recognition, algorithms and filters
(100.4999) Image processing : Pattern recognition, target tracking

ToC Category:
Image Processing

Original Manuscript: October 15, 2012
Manuscript Accepted: January 2, 2013
Published: February 19, 2013

Virtual Issues
Vol. 8, Iss. 3 Virtual Journal for Biomedical Optics

Jared Herweg, John Kerekes, and Michael Eismann, "Separability between pedestrians in hyperspectral imagery," Appl. Opt. 52, 1330-1338 (2013)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. J. R. Schott, Remote Sensing, 2nd ed. (Oxford University, 2007).
  2. J. Blackburn, M. Mendenhall, A. Rice, P. Shelnutt, N. Soliman, and J. Vasquez, “Feature aided tracking with hyperspectral imagery,” Proc. SPIE 6699, 1–12 (2007). [CrossRef]
  3. A. Rice, J. Vasquez, M. Mendenhall, and J. Kerekes, “Feature-aided tracking via synthetic hyperspectral imagery,” in First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2009 (IEEE, 2009), pp. 1–4.
  4. A. S. Nunez, “A physical model of human skin and its application for search and rescue,” Ph.D. dissertation, Air Force Institute of Technology, Wright–Patterson Air Force Base, OH (2010).
  5. J. D. Clark, M. J. Mendenhall, and G. L. Peterson, “Stochastic feature selection with distributed feature spacing for hyperspectral data,” in 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) 2010 (IEEE, 2010), pp. 1–4.
  6. J. A. Herweg, J. P. Kerekes, and M. Eismann, “Hyperspectral imaging of natural signatures for pedestrians,” Proc. SPIE 8390, 83901C (2012). [CrossRef]
  7. C. M. Jengo and J. LaVeigne, “Sensor performance comparison of HyperSpecTIR instruments 1 and 2,” in Aerospace Conference 2004 Proceedings (IEEE, 2004), Vol. 3, pp. 1799–1805.
  8. Analytical Spectral Devices, Inc., “FieldSpec Pro User’s Guide,” 2002, retrieved, 15 October 2010, http://www.asdi.com .
  9. D. Simmons, “Performance characterization of an innovative illumination source for the analytical spectral device spectroradiometer, FieldSpec Pro FR,” Rochester Institute of Technology Digital Imaging and Remote Sensing Laboratory Probe Development Status Report (2006).
  10. I. Pavlidis, P. Symosek, B. Fritz, M. Bazakos, and N. Papanikolopoulos, “Automatic detection of vehicle occupants: the imaging problem and its solution,” Machine Vis. Appl. 11, 313–320 (2000). [CrossRef]
  11. T. L. Haran, “Short-wave infrared diffuse reflectance of textile materials,” Masters’ thesis (Georgia State University, 2008).
  12. P. Bajorski, Statistics for Imaging, Optics, and Photonics (Wiley, 2011).
  13. A. Webb, Statistical Pattern Recognition, 2nd ed. (Wiley, 2005).
  14. J. A. Herweg, “Pedestrian detection phenomenology in a cluttered urban environment using hyperspectral imaging,” Ph.D. dissertation (Rochester Institute of Technology, 2012).
  15. R. S. Berns, Billmeyer and Saltzman’s Principles of Color Technology, 3rd ed. (Wiley, 2000).
  16. E. J. Ientilucci and P. Bajorski, “Stochastic modeling of physically derived signature spaces,” J. Appl. Remote Sens. 2, 1–10 (2008). [CrossRef]

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