• Adaptive Feature-Specific Spectral Imaging Classifier (AFSSI-C)

      Gehm, Michael; Dunlop, Matthew; Poon, Phillip; University of Arizona (International Foundation for Telemetering, 2013-10)
      The AFSSI-C is a spectral imager that generates spectral classification directly, in fewer measurements than are required by traditional systems that measure the spectral datacube (which is later interpreted to make material classification). By utilizing adaptive features to constantly update conditional probabilities for the different hypotheses, the AFSSI-C avoids the overhead of directly measuring every element in the spectral datacube. The system architecture, feature design methodology, simulation results, and preliminary experimental results are given.
    • Calibration of High Dimensional Compressive Sensing Systems: A Case Study in Compressive Hyperspectral Imaging

      Gehm, Michael; Poon, Phillip; Dunlop, Matthew; University of Arizona (International Foundation for Telemetering, 2013-10)
      Compressive Sensing (CS) is a set of techniques that can faithfully acquire a signal from sub- Nyquist measurements, provided the class of signals have certain broadly-applicable properties. Reconstruction (or exploitation) of the signal from these sub-Nyquist measurements requires a forward model - knowledge of how the system maps signals to measurements. In high-dimensional CS systems, determination of this forward model via direct measurement of the system response to the complete set of impulse functions is impractical. In this paper, we will discuss the development of a parameterized forward model for the Adaptive, Feature-Specific Spectral Imaging Classifier (AFSSI-C), an experimental compressive spectral image classifier. This parameterized forward model drastically reduces the number of calibration measurements.