Recently, a hyperspectral imaging system (HIS) with a Fourier Transform InfraRed (FTIR) spectrometer has been widely used due to its strengths in detecting gaseous fumes. Even though numerous algorithms for detecting gaseous fumes have already been studied, it is still difficult to detect target gases properly because of atmospheric interference substances and unclear characteristics of low concentration gases. In this paper, we propose detection algorithms for classifying hazardous gases using a deep neural network (DNN) and a convolutional neural network (CNN). In both the DNN and CNN, spectral signal preprocessing, e.g., offset, noise, and baseline removal, are carried out. In the DNN algorithm, the preprocessed spectral signals are used as feature maps of the DNN with five layers, and it is trained by a stochastic gradient descent (SGD) algorithm (50 batch size) and dropout regularization (0.7 ratio). In the CNN algorithm, preprocessed spectral signals are trained with 1 × 3 convolution layers and 1 × 2 max-pooling layers. As a result, the proposed algorithms improve the classification accuracy rate by 1.5% over the existing support vector machine (SVM) algorithm for detecting and classifying hazardous gases.
A hyperspectral imaging system (HIS) with a Fourier transform infrared (FTIR) spectrometer is an excellent method for the detection and identification of gaseous fumes. Various detection algorithms can remove background spectra from measured spectra and determine the degree of spectral similarity between the extracted signature and reference signatures of target compounds. However, given the interference signatures caused by FTIR instruments, it is impossible to extract the spectral signatures of target gases perfectly. Such interference signatures degrade the detection performance. In this paper, a detection algorithm for gaseous fumes using a multiclass support vector machine (SVM) classifier is proposed. The proposed algorithm has a training step and a test step. In the training step, the spectral signatures are extracted from measured spectra which are labeled. Then, hyperplanes which classify gas spectra are trained and the multiclass SVM classifier outcomes are calculated using the hyperplanes. In the test step, spectral signatures extracted from unknown measured spectra are substituted to the SVM classifier, after which the detection result is obtained. This multiclass SVM classifier robustly responds to performance degradation caused by unremoved interference signatures because it trains not only gaseous signatures but also the related interference signatures. The experimental results verify that the algorithm can effectively detect hazardous clouds.
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