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

## TL;DR

This paper demonstrates that compressive sensing sampling of hyperspectral data cubes can unexpectedly amplify chemical signals, often making them more detectable than in fully sampled data, with potential practical benefits.

## Contribution

It reveals a novel phenomenon where bandwise compressive sensing enhances chemical signal detection in hyperspectral data, supported by real-world dataset analysis and theoretical discussion.

## Key findings

- Chemical signals can be amplified through CS sampling and reconstruction.
- Amplification of signals increases as sampling becomes more sparse.
- CS-based reconstruction can outperform full sampling in chemical detection.

## Abstract

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.