Tracking of vehicles across multiple radiance and reflectance hyperspectral datasets

Emmett J. Ientilucci; Stefania Matteoli; John P. Kerekes

doi:10.1117/12.820348

27 April 2009 Tracking of vehicles across multiple radiance and reflectance hyperspectral datasets

Emmett J. Ientilucci, Stefania Matteoli, John P. Kerekes

Proceedings Volume 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV; 73340A (2009) https://doi.org/10.1117/12.820348
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

Tracking of vehicles, in both radiance and reflectance hyperspectral imagery, was analyzed using traditional reflectance domain and forward predicting physics based target detection algorithms. The investigation consisted of locating vehicles in one image followed by locating the same (relocated) vehicles in another image that was collected at a different time. Traditional approaches to hyperspectral target detection involve the application of detection algorithms to atmospherically compensated imagery. Rather than compensate the imagery, a more recent approach uses physical models to generate radiance signature spaces. The signature space is actually a representation of what the target might look like to the sensor as the reflectance propagates through the atmosphere. The model takes into account atmospherics, illumination conditions and target reflectivity. Different collection times have varying atmospheric conditions leading to changes in the sensor-reaching radiance spectrum. A forward predicting physical model, applied in the radiance domain, handles this variation. The data used in this study is a new ground truthed hyperspectral data set, collect with the airborne HyMap sensor, and which is now freely available to the community through the web for evaluation, testing, and algorithm development. Vehicles were relocated across various flight lines. This paper compares vehicle detection results, across these multiple images collected within a short time period, using both the physical model and the traditional atmospheric compensation approach.

Citation Download Citation

Emmett J. Ientilucci, Stefania Matteoli, and John P. Kerekes "Tracking of vehicles across multiple radiance and reflectance hyperspectral datasets", Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73340A (27 April 2009); https://doi.org/10.1117/12.820348

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