Abstract

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

Full Article  |  PDF Article

References

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
Login to access OSA Member Subscription

Cited By

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
Login to access OSA Member Subscription

Figures (13)

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
Login to access OSA Member Subscription

Tables (3)

You do not have subscription access to this journal. Article tables 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
Login to access OSA Member Subscription

Equations (3)

You do not have subscription access to this journal. Equations 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
Login to access OSA Member Subscription

Metrics

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
Login to access OSA Member Subscription