Browse > Article
http://dx.doi.org/10.9766/KIMST.2018.21.6.744

Fast Remote Detection Algorithms for Chemical Gases Using Pre-Detection with a Passive FTIR Spectrometer  

Yu, Hyeonggeun (School of Electrical Engineering, Korea Advanced Institute of Science and Technology)
Park, Dongjo (School of Electrical Engineering, Korea Advanced Institute of Science and Technology)
Nam, Hyunwoo (The 4th Research and Development Institute, Agency for Defense Development)
Park, Byeonghwang (The 4th Research and Development Institute, Agency for Defense Development)
Publication Information
Journal of the Korea Institute of Military Science and Technology / v.21, no.6, 2018 , pp. 744-751 More about this Journal
Abstract
In this paper, we propose a fast detection and identification algorithm of chemical gases with a passive FTIR spectrometer. We use a pre-detection algorithm that can reduce the spatial region effectively for gas detection and the candidates of the target. It is possible to remove background spectra effectively from measured spectra with the least-squares method. The CC(Correlation Coefficients) and the SNR(Signal-to-Noise Ratio) methods are used for the detection of target gases. The proposed pre-detection algorithm allows the total process of chemical gas detection to be performed with lower complexity compared with the conventional algorithms. This paper can help developing real-time chemical detection instruments and various applications of FTIR spectrometers.
Keywords
FTIR Spectrometry; Chemical Detection; Hyperspectral Imaging; Remote Sensing;
Citations & Related Records
연도 인용수 순위
  • Reference
1 C. C. Funk, J. Theiler, D. A. Roberts and C. C. Borel, “Clustering to Improve Matched Filter Detection of Weak Gas Plumes in Hyperspectral Thermal Imagery,” IEEE Trans. Geosci. Remote Sens., Vol. 39, No. 7, pp. 1410-1420, Jul. 2001.   DOI
2 D. Manolakis and G. A. Shaw, "Detection Algorithms for Hyperspectral Imaging Applications," IEEE Signal Process. Mag., Vol. 19, Issue 1, pp. 29-43, Jan. 2002.   DOI
3 S. Walsh, L. Chilton, M. Tardiff and C. Metoyer, "Effect of Background Emissivity on Gas Detection in Thermal Hyperspectral Imagery," Pacific Northwest National Laboratory, PNNL-17874, pp. 29-43, Jul. 2008.
4 M. L. Polak, J. L. Hall and K. C. Herr, “Passive Fourier-Transform Infrared Spectroscopy of Chemical Plumes: An Algorithm for Quantitative Interpolation and Real-Time Background Removal,” Applied Optics, Vol. 34, No. 24, pp. 5406-5412, Aug 1992.   DOI
5 N. Keshava and J. F. Mustard, “Spectral Unmixing,” IEEE Signal Process. Mag., Vol. 19, No. 1, pp. 44- 57, Jan. 2002.   DOI
6 R. Harrig, “Passive Remote Sensing of Pollutant Clouds by FTIR Spectrometry: Signal-to-Noise Ratio as a Function of Spectral Resolution,” Applied Optics, Vol. 43, No. 23, pp. 4603-4610, Aug. 2004.   DOI
7 K. Tan, E. Li, Q. Du and P. Du, “Hyperspectral Image Classification Using Band Selection and Morphological Profiles,” IEEE J. Sel. Topics in Appl. Earth Observ. Remote Sens., Vol. 7, No. 1, pp. 40-48, Jan. 2014.   DOI
8 A. Beil, R. Daum, R. Harig and G. Matz, "Remote Sensing of Atmospheric Pollution by Passive FTIR Spectrometry," in Proc. of SPIE, Vol. 3493, pp. 32-43, Sep. 1998.
9 D. Manolakis, S. E. Golowich and R. S. DiPietro, “Long-Wave Infrared Hyperspectral Remote Sensing of Chemical Clouds: A Focus on Signal Processing Approaches,” IEEE Signal Process. Mag., Vol. 31, No. 4, pp. 120-141, Jul. 2014.   DOI
10 D. H. Kim, “FTIR for Remote Chemical Detection,” Journal of the Korea Institute of Military Science and Technology, Vol. 18, No. 1, pp. 153-169, Jun. 2011.
11 D. F. Flanigan, "Prediction of the Limits of Detection of Harzardous Vapors by Passive Infrared with the Use of MODTRAN," Applied Optics, Vol. 35, Issue 30, pp. 6368-6374, Oct. 1996.
12 G. Chen and S. E. Qian, “Denoising of Hyperspectral Imagery using Principal Component Analysis and Wavelet Shrinkage,” IEEE Trans. Geosci. Remote Sens., Vol. 49, No. 3, pp. 973-980, Mar. 2011.   DOI
13 R. Harrig and G. Matz, "Toxic Cloud Imaging by Infrared Spectrometry: A Scanning FTIR System for Identification and Visualization," Field Analytical Chemistry and Technology, Vol. 5, Issue 1-2, pp. 75-90, May 2001.   DOI
14 A. Vallieres, A. Villemaire, M. Chamberland, L. Belhumeur, V. Farley, J. Giroux and J. F. Legault, "Algorithms for Chemical Detection, Identification and Quantification for Thermal Hyperspectral Imagers," in Proc. of SPIE, Vol. 5995, pp. 147-157, Nov. 2005.
15 V. Farley, A. Vallieres, A. Villemaire, M. Chamberland, P. Lagueux and J. Giroux, "Chemical Agent Detection and Identification with a Hyperspectral Imaging Infrared Sensor," in Proc. of SPIE, Vol. 5995, pp. 6739-6749, Sep. 2007.
16 D. Cerra, R. Muller and P. Reinartz, “Noise Reduction in Hyperspectral Images through Spectral Unmixing,” IEEE Geosci. Remote Sens. Lett., Vol. 11, No. 1, pp. 109-113, Jan. 2014.   DOI
17 W. Li, S. Prasad and J. E. Fowler, “Noise-adjusted Subspace Discriminant Analysis for Hyperspectral Imagery Classification,” IEEE Geosci. Remote Sens. Lett., Vol. 10, No. 6, pp. 1374-1378, Nov. 2013.   DOI
18 N. Renard, S. Bourennane and J. B. Talon, “Denoising and Dimensionality Reduction using Multilinear Tools for Hyperspectral Images,” IEEE Geosci. Remote Sens. Lett., Vol. 5, No. 2, pp. 138- 142, Apr. 2008.   DOI