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24 April 2020 More chemical detection through less sampling: amplifying chemical signals in hyperspectral data cubes through compressive sensing
Henry Kvinge, Elin Farnell, Julia R. Dupuis, Michael Kirby, Chris Peterson, Elizabeth C. Schundler
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Proceedings Volume 11392, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXVI; 113920N (2020) https://doi.org/10.1117/12.2557030
Event: SPIE Defense + Commercial Sensing, 2020, Online Only
Abstract
Compressive sensing (CS) is a method of sampling which permits some classes of signals to be reconstructed with high accuracy even when they have been undersampled. 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-Pérot 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.
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Henry Kvinge, Elin Farnell, Julia R. Dupuis, Michael Kirby, Chris Peterson, and Elizabeth C. Schundler "More chemical detection through less sampling: amplifying chemical signals in hyperspectral data cubes through compressive sensing", Proc. SPIE 11392, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXVI, 113920N (24 April 2020); https://doi.org/10.1117/12.2557030
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KEYWORDS
Hyperspectral imaging
Signal detection
Chemical detection
Compressed sensing
Reconstruction algorithms
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