2019/06/27 by Henry Kvinge, Elin Farnell, Kvinge, Henry +9
Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Microwave Imaging and Scattering Analysis #Microwave and Dielectric Measurement Techniques #Signal Processing (eess.SP) #Sparse and Compressive Sensing Techniques #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1906.11818
openalex publication_date 2019/06/27 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28
Compressive sensing (CS) is a method of sampling which permits some classes\nof signals to be reconstructed with high accuracy even when they were\nunder-sampled. In this paper we explore a phenomenon in which bandwise CS\nsampling of a hyperspectral data cube followed by reconstruction can actually\nresult in amplification of chemical signals contained in the cube. Perhaps most\nsurprisingly, chemical signal amplification generally seems to increase as the\nlevel of sampling decreases. In some examples, the chemical signal is\nsignificantly stronger in a data cube reconstructed from 10% CS sampling than\nit is in the raw, 100% sampled data cube. We explore this phenomenon in two\nreal-world datasets including the Physical Sciences Inc. Fabry-P 'erot\ninterferometer sensor multispectral dataset and the Johns Hopkins Applied\nPhysics Lab FTIR-based longwave infrared sensor hyperspectral dataset. Each of\nthese datasets contains the release of a chemical simulant, such as glacial\nacetic acid, triethyl phospate, and sulfur hexafluoride, and in all cases we\nuse the adaptive coherence estimator (ACE) to detect a target signal in the\nhyperspectral data cube. We end the paper by suggesting some theoretical\njustifications for why chemical signals would be amplified in CS sampled and\nreconstructed hyperspectral data cubes and discuss some practical implications.\n