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.
A data-driven approach to sampling matrix selection for compressive sensing
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abstract
Sampling is a fundamental aspect of any implementation of compressive sensing. Typically, the choice of sampling method is guided by the reconstruction basis. However, this approach can be problematic with respect to certain hardware constraints and is not responsive to domain-specific context. We propose a method for defining an order for a sampling basis that is optimal with respect to capturing variance in data, thus allowing for meaningful sensing at any desired level of compression. We focus on the Walsh-Hadamard sampling basis for its relevance to hardware constraints, but our approach applies to any sampling basis of interest. We illustrate the effectiveness of our method on the Physical Sciences Inc. Fabry-P\'{e}rot interferometer sensor multispectral dataset, the Johns Hopkins Applied Physics Lab FTIR-based longwave infrared sensor hyperspectral dataset, and a Colorado State University Swiss Ranger depth image dataset. The spectral datasets consist of simulant experiments, including releases of chemicals such as GAA and SF6. We combine our sampling and reconstruction with the adaptive coherence estimator (ACE) and bulk coherence for chemical detection and we incorporate an algorithmic threshold for ACE values to determine the presence or absence of a chemical. We compare results across sampling methods in this context. We have successful chemical detection at a compression rate of 90%. For all three datasets, we compare our sampling approach to standard orderings of sampling basis such as random, sequency, and an analog of sequency that we term `frequency.' In one instance, the peak signal to noise ratio was improved by over 30% across a test set of depth images.
fields
eess.IV 2years
2019 2verdicts
UNVERDICTED 2representative citing papers
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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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.