  Compressive sensing (CS) is a method of sampling which permits some classes
of signals to be reconstructed with high accuracy even when they were
under-sampled. In this paper we explore a phenomenon in which bandwise CS
sampling of a hyperspectral data cube followed by reconstruction can actually
result in amplification of chemical signals contained in the cube. Perhaps most
surprisingly, chemical signal amplification generally seems to increase as the
level of sampling decreases. In some examples, the chemical signal is
significantly stronger in a data cube reconstructed from 10% CS sampling than
it is in the raw, 100% sampled data cube. We explore this phenomenon in two
real-world datasets including the Physical Sciences Inc. Fabry-P\'{e}rot
interferometer sensor multispectral dataset and the Johns Hopkins Applied
Physics Lab FTIR-based longwave infrared sensor hyperspectral dataset. Each of
these datasets contains the release of a chemical simulant, such as glacial
acetic acid, triethyl phospate, and sulfur hexafluoride, and in all cases we
use the adaptive coherence estimator (ACE) to detect a target signal in the
hyperspectral data cube. We end the paper by suggesting some theoretical
justifications for why chemical signals would be amplified in CS sampled and
reconstructed hyperspectral data cubes and discuss some practical implications.
