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Proceedings Paper

Autonomous target dependent waveband selection for tracking in performance-driven hyperspectral sensing
Author(s): Sabino M. Gadaleta; John P. Kerekes; Kyle M. Tarplee
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Paper Abstract

Performance-driven sensing is a promising new concept that relies on sensing, processing, and exploiting only the most "decision-relevant" sets of target data for the purpose of reducing requirements on data collection, processing, and communications. An example of a device supporting such a concept is a MEMS-based single pixel Fabry-Perot spectrometer being developed at the Rochester Institute of Technology, which can record selected wavelengths on a per-pixel basis throughout an image. This paper presents an autonomous target-dependent waveband selection approach for performance-driven sensing with an adaptive hyperspectral imaging sensor. Given a target that is to be tracked, a subset of wavebands is estimated from locally recorded hyperspectral data that provides optimal target detectability against local background. The waveband selection algorithm relies on finding a subset of bands that provides maximum separation between a target histogram and local background histogram constructed from the respective bands. To illustrate the concept, we perform a simulation study for vehicle tracking in a set of synthetic DIRSIG rendered HSI images. The simulations demonstrate improved vehicle tracking accuracy when using the adaptively-selected subset of wavebands for tracking by histogram matching compared to performing tracking by histogram matching with regular (fixed) color bands. We extend the framework to a dynamic concept where the waveband subset is updated over time as a function of position estimation accuracy and discuss the full integration of the Feature-Aided Tracking (FAT) component derived from the selected wavebands within a Multiple Hypothesis Tracking (MHT) framework.

Paper Details

Date Published: 24 May 2012
PDF: 14 pages
Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 839023 (24 May 2012); doi: 10.1117/12.916978
Show Author Affiliations
Sabino M. Gadaleta, Numerica Corp. (United States)
John P. Kerekes, Rochester Institute of Technology (United States)
Kyle M. Tarplee, Numerica Corp. (United States)

Published in SPIE Proceedings Vol. 8390:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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