Raman spectroscopy, which analyzes a Raman scattering spectrum of a target, has emerged as a key technology for non-contact chemical agent (CA) detection. Many CA detection algorithms based on Raman spectroscopy have been studied. However, the baseline, which is caused by fluorescence generated when measuring the Raman scattering spectrum, degrades the performance of CA detection algorithms. Therefore, we propose a baseline correction algorithm that removes the baseline, while minimizing the distortion of the Raman scattering spectrum. Assuming that the baseline is a linear combination of broad Gaussian vectors, we model the measured spectrum as a linear combination of broad Gaussian vectors, bases of background materials and the reference spectra of target CAs. Then, we estimate the baseline and Raman scattering spectrum together using the least squares method. Design parameters of the broad Gaussian vectors are discussed. The proposed algorithm requires reference spectra of target CAs and the background basis matrix. Such prior information can be provided when applying the CA detection algorithm. Via the experiment with real CA spectra measured by the Raman spectrometer, we show that the proposed baseline correction algorithm is more effective for removing the baseline and improving the detection performance, than conventional baseline correction algorithms.

Target detection and classification by Raman spectroscopy are important techniques for biological and chemical defense in military operations. Conventionally, these techniques preprocess the observed spectra using smoothing or baseline correction and apply detection algorithms like the generalized likelihood ratio test, independent component analysis, nonnegative matrix factorization, etc. These conventional detection algorithms need preprocessing and multiple shots of Raman spectra to get a reasonable accuracy. Recently, techniques based on deep learning are being used for target detection and classification due to its great adaptability and high accuracy over other methods and due to no requirement for preprocessing. Deep learning may give a good performance, but need retraining when untrained class targets are introduced which is time-consuming and bothersome. We devise a novel algorithm using a variant of the pseudo-Siamese network, one of the deep learning algorithms, that does not need retraining to detect and classify untrained class targets. Our algorithm detects and classifies targets with only one shot. In addition, our algorithm does not need preprocessing. We verify our algorithm with Raman spectra measured using a Raman spectrometer.

We propose a hard expectation-maximization-based normalized matched filter (EM-NMF) for the detection of chemical warfare agent (CWA) clouds under background contamination. The NMF, which is one of the most powerful detectors, requires background statistics calculated from a background training dataset. However, in practice, because the training dataset is likely to contain CWA-on background pixels, the performance of the NMF is severely degraded. This phenomenon is referred to as background contamination. To address this issue, we propose an algorithm that estimates the posterior probability of each pixel belonging to either the background or the CWA class. The optimal posterior probabilities are obtained by maximizing the log-likelihood of a contaminated dataset using the EM algorithm. Based on the posterior probability, we extract CWA-free background pixels from the contaminated dataset and design a {hard EM-NMF} with extracted CWA-free background pixels. We demonstrate that the proposed algorithm is an effective solution for background contamination, via experimental results conducted with actual CWA data measured by a Bruker HI--90 instrument in an outdoor setting as well as synthetic CWA data.

A matched filter (MF) is one of the most widely used detectors for the detection of chemical agent (CA) clouds in the passive hyperspectral imaging system. To improve the detection performance of the MF, a linear cooperation scheme that allocates cooperation coefficients to the spectra of the neighbouring pixels is proposed. The optimal cooperation coefficients, which removes noise signatures whilst minimising the distortion of CA signatures, are acquired by finding the maximum likelihood estimator of the cooperation coefficients. It is proved that a moving average scheme that assigns the same coefficients is the optimal cooperation scheme. Finally, a cooperative MF with the optimal cooperation scheme is designed. It is demonstrated that the proposed cooperative MF is capable of robust detection performance via outdoor experiments with actual CA data measured by a Bruker HI‐90 instrument.

A passive Fourier transform infrared (FTIR) spectrometer is an instrument that can detect and identify chemical contaminants. An FTIR spectrometer exploits the infrared radiation of the surrounding terrain as a light source and receives a mixed signal of background signal, gas signal, and noise. The performance of most detection algorithms for detecting gaseous plumes, such as the normalized matched filter (NMF) and adaptive subspace detector (ASD), deteriorates due to the noise generated by an FTIR spectrometer. In this paper, a noise reduction algorithm based on the maximum noise fraction (MNF) transform to improve the performance of hazardous gas detection algorithms is proposed. We apply the MNF transform to the measured spectra and remove the high noise fraction component. Then the noise-reduced spectra are restored by conducting the inverse MNF transform. The experimental results show that the proposed algorithm reduces the noise and enhances the gas detection performance.

Fourier transform infrared (FTIR) hyperspectral imaging systems are deployed in various fields where spectral information is exploited. Chemical warfare agent (CWA) detection is one of such fields and it requires a fast and accurate process from the measurement to the visualization of detection results, including noise removal. A general concern of existing noise removal algorithms is a trade-off between time and performance. This paper suggests a minimum mean square error (MMSE) approach as an efficient noise removal algorithm for FTIR hyperspectral images. The experimental result shows that the MMSE estimator spends less time to achieve comparable performance to the existing algorithms.