Efficient Chemical-Warfare-Agent Detection Algorithm for FTIR-Based Hyperspectral Imagery Using SVM Classifier

Abstract

The hyperspectral imaging system (HIS) using a Fourier transform infrared (FTIR) spectrometer is one of the key technologies for detection and identification of chemical warfare agents (CWAs). Recently, various detection algorithms based on machine learning techniques have been studied. These algorithms are robust against performance degradation caused by noise signatures generated by FTIR instruments. However, interference signatures from background materials degrade detection performance. In this paper, we propose an efficient algorithm that uses a support vector machine (SVM) classifier to detect CWAs. In contrast to the conventional algorithms that use measured spectra to train the SVM classifier, the proposed algorithm trains the SVM classifier using CWA signatures obtained by removing background signatures from measured spectra. Therefore, the proposed algorithm is robust against the performance degradation induced by interference signatures from background materials. Experimental results verify that the algorithm can detect CWA clouds effectively.