Hyperspectral object tracking using small sample size

Dalton Rosario; Henry Kling

doi:10.1117/12.850359

12 May 2010 Hyperspectral object tracking using small sample size

Dalton Rosario, Henry Kling

Proceedings Volume 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI; 76950I (2010) https://doi.org/10.1117/12.850359
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

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

Citation Download Citation

Dalton Rosario and Henry Kling "Hyperspectral object tracking using small sample size", Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 76950I (12 May 2010); https://doi.org/10.1117/12.850359

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