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) |
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 |