24 May 2012 Autonomous target dependent waveband selection for tracking in performance-driven hyperspectral sensing
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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.
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Sabino M. Gadaleta, Sabino M. Gadaleta, John P. Kerekes, John P. Kerekes, Kyle M. Tarplee, Kyle M. Tarplee, } "Autonomous target dependent waveband selection for tracking in performance-driven hyperspectral sensing", Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 839023 (24 May 2012); doi: 10.1117/12.916978; https://doi.org/10.1117/12.916978

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