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 prop- erties. 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.
|Journal||Proceedings of the International Telemetering Conference|
|State||Published - 1 Dec 2013|
|Event||ITC/USA 2013: 49th Annual International Telemetering Conference and Technical Exhibition - Meeting all the Challenges of Telemetry, 2013 - Las Vegas,NV, United States|
Duration: 21 Oct 2013 → 24 Oct 2013
- Compressive sensing
- Hyperspectral imaging