Vehicle tracking with multi-temporal hyperspectral imagery

John Kerekes; Michael Muldowney; Kristin Strackerjan; Lon Smith; Brian Leahy

doi:10.1117/12.666121

4 May 2006 Vehicle tracking with multi-temporal hyperspectral imagery

John Kerekes, Michael Muldowney, Kristin Strackerjan, Lon Smith, Brian Leahy

Author Affiliations +

Proceedings Volume 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII; 62330C (2006) https://doi.org/10.1117/12.666121
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

Abstract

Hyperspectral imagery has the capability of capturing spectral features of interest that can be used to differentiate among similar materials. While hyperspectral imaging has been demonstrated to provide data that enable classification of relatively broad categories, there remain open questions as to how fine of discrimination is possible. An application of this fine discrimination question is the potential that spectral features exist in the surface reflectance of ordinary civilian vehicles that would enable tracking of a particular vehicle across repeated hyperspectral images in a cluttered urban area. To begin to explore this question a vehicle tracking experiment was conducted in the summer of 2005 on the Rochester Institute of Technology (RIT) campus in Rochester, New York. Several volunteer vehicles were moved around campus at specific times coordinated with over flights of RIT's airborne Modular Imaging Spectrometer Instrument (MISI). MISI collected sequential images of the campus in 70 spectral channels from 0.4 to 1.0 microns with a ground resolution of approximately 2.5 meters. Ground truth spectra and photographs were collected for the vehicles. These data are being analyzed to determine the ability to uniquely associate a vehicle in one image with its location in a subsequent image. Initial results have demonstrated that the spectral measurement of a specific vehicle can be used to find the same vehicle in a subsequent image, although this is not always possible and is very dependent upon the specifics of the situation. Additionally, efforts are presented that explore predicted performance for variations in scene and sensor parameters through an analytical performance prediction model.

Citation Download Citation

John Kerekes, Michael Muldowney, Kristin Strackerjan, Lon Smith, and Brian Leahy "Vehicle tracking with multi-temporal hyperspectral imagery", Proc. SPIE 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, 62330C (4 May 2006); https://doi.org/10.1117/12.666121

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