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Compressive sensing (CS) is a method of sampling which permits some classesof signals to be reconstructed with high accuracy even when they wereunder-sampled. In this paper we explore a phenomenon in which bandwise CSsampling of a hyperspectral data cube followed by reconstruction can actuallyresult in amplification of chemical signals contained in the cube. Perhaps mostsurprisingly, chemical signal amplification generally seems to increase as thelevel of sampling decreases. In some examples, the chemical signal issignificantly stronger in a data cube reconstructed from 10% CS sampling thanit is in the raw, 100% sampled data cube. We explore this phenomenon in tworeal-world datasets including the Physical Sciences Inc. Fabry-P\'{e}rotinterferometer sensor multispectral dataset and the Johns Hopkins AppliedPhysics Lab FTIR-based longwave infrared sensor hyperspectral dataset. Each ofthese datasets contains the release of a chemical simulant, such as glacialacetic acid, triethyl phospate, and sulfur hexafluoride, and in all cases weuse the adaptive coherence estimator (ACE) to detect a target signal in thehyperspectral data cube. We end the paper by suggesting some theoreticaljustifications for why chemical signals would be amplified in CS sampled andreconstructed hyperspectral data cubes and discuss some practical implications.

More chemical detection through less sampling: amplifying chemical signals in hyperspectral data cubes through compressive sensing