Compressive sensing reconstruction amplifies chemical signals in hyperspectral cubes, with greater amplification at lower sampling rates, demonstrated on two real chemical simulant datasets using ACE detection.
Multipulse adaptive coherence for detection in wind turbine clutter
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A variance-based ordering of the Walsh-Hadamard sampling basis for compressive sensing outperforms standard orderings, enabling successful chemical detection at 90% compression and over 30% PSNR improvement on depth images.
Empirical comparison on two real chemical-release datasets shows L1 regularization yields better ACE-based chemical detection than total variation at 90% compression.
citing papers explorer
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More chemical detection through less sampling: amplifying chemical signals in hyperspectral data cubes through compressive sensing
Compressive sensing reconstruction amplifies chemical signals in hyperspectral cubes, with greater amplification at lower sampling rates, demonstrated on two real chemical simulant datasets using ACE detection.
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A data-driven approach to sampling matrix selection for compressive sensing
A variance-based ordering of the Walsh-Hadamard sampling basis for compressive sensing outperforms standard orderings, enabling successful chemical detection at 90% compression and over 30% PSNR improvement on depth images.
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Total variation vs L1 regularization: a comparison of compressive sensing optimization methods for chemical detection
Empirical comparison on two real chemical-release datasets shows L1 regularization yields better ACE-based chemical detection than total variation at 90% compression.