Hyperspectral object tracking using small sample size

摘要

This paper introduces a simple approach for object tracking using hyperspectral (HS) spectral features. The approachaddresses the object tracking problem using a small object sample size. For a particular application, the keychallenges are: (i) Offline training cannot be utilized; (ii) motor vehicles of interest (targets) have a smallsample size (e.g., less than 9); and (iii) kinematic states of targets cannot be used for tracking, sincestationary targets are also of interest. Using HS imagery, this paper introduces a method that exploits the mean andmedian averages spectra to estimate higher moments of the underlying (and unknown) probability distribution functionof spectra; in particular, skew tendency and sign. Tracking HS targets is then possible using this algorithm to test asequence of HS imagery, given that target spectra are initially cued by the user. The approach was implemented into acommercially off the shelf workstation, featuring the IBM Cell Processor and GA-180 Add in Board. Preliminaryresults are promising using a challenging HS data cube.