title: Separability between pedestrians in hyperspectral imagery.
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.
authors: Air Force Institute of Technology, Dayton, Ohio 45433, USA. jxh6389@cis.rit.edu
authors: Jared
authors: J
authors: Herweg
authors: John
authors: J
authors: Kerekes
authors: Michael
authors: M
authors: Eismann
mesh_terms: D000465:Algorithms; D003020:Clothing; D006197:Hair; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D010783:Photometry / Q000295:instrumentation*; D011825:Radar / Q000295:instrumentation*; D058998:Remote Sensing Technology / Q000295:instrumentation*; D015203:Reproducibility of Results; D012867:Skin / Q000473:pathology*; D013057:Spectrum Analysis / Q000295:instrumentation; D016138:Walking