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.
Hyeong-Geun Yu, Jae-Hoon Lee, Yong-Chan Kim, and Dong-Jo Park, "Intelligent detection algorithm of hazardous gases for FTIR-based hyperspectral imaging system using SVM classifier," Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 1019808 (Presented at SPIE Defense + Security: April 11, 2017; Published: 5 May 2017); https://doi.org/10.1117/12.2262066.
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