Acta Optica Sinica, Volume. 34, Issue 7, 730002(2014)
Adaptive Detection for Pollutant Gases Based on Orthogonal Subspace Projection
[1] [1] Vincent Farley, Alexandre Vallières, Martin Chamberland, et al.. Performance of the FIRST, a longwave infrared hyperspectral imaging sensor [C]. SPIE, 2006, 6398: 63980T.
[2] [2] Roland Harig, Peter Rusch, Chris Dyer, et al.. Remote measurement of highly toxic vapours by scanning imaging Fourier-transform spectrometry [C]. SPIE, 2005, 5995: 599510.
[3] [3] D Manolakis, L G Jairam, D Zhang, et al.. Statistical models for LWIR hyperspectral backgrounds and their applications in chemical agent detection [C]. SPIE, 2007, 6565: 656525.
[4] [4] Dimitris Manolakis, Gary Shaw. Detection algorithms for hyperspectral imaging applications [J]. IEEE Signal Processing Magazine, 2002, 19(1): 29-43.
[5] [5] Dimitris Manolakis, David Marden, Gary A Shaw. Hyperspectral image processing for automatic target detection applications [J]. Lincoln Laboratory Journal, 2003, 14(1): 79-116.
[6] [6] Tom Burr, Nicolas Hengartner. Overview of physical models and statistical approaches for weak gaseous plume detection using passive infrared hyperspectral imagery [J]. Sensors, 2006, 6(12): 1721-1750.
[7] [7] D Manolakis. Signal processing algorithms for hyperspectral remote sensing of chemical plumes [J]. IEEE Transactions Geoscience and Remote Sensing, 2008. 1857-1860.
[8] [8] J C Harsanyi, Chein-I Chang. Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach [J]. IEEE Transactions Geoscience and Remote Sensing, 1994, 32(4): 779-785.
[9] [9] Chein-I Chang. Orthogonal subspace projection (OSP) revisited: a comprehensive study and analysis [J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(3): 502~518.
[11] [11] M Kay Steven. Fundamentals of Statistical Signal Processing: Detection Theory [M]. New Jersey: Prentice Hall, 1998.
[12] [12] Shawn Kraut, Louis L Scharf, L Todd McWhorter. Adaptive subspace detectors [J]. IEEE Transactions on Signal Processing, 2001, 49(1): 1-16.
[13] [13] Dimitris Manolakis, Francis M D′Amico. A taxonomy of algorithms for chemical vapor detection with hyperspectral imaging spectroscopy [C]. SPIE, 2005, 5795: 125-133.
[14] [14] Shawn Kraut, Louis L Scharf. The CFAR adaptive subspace detector is a scale-invariant GLRT [J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 47(9): 2538-2541.
[15] [15] Chein-I Chang, Qian Du. Estimation of number of spectrally distinct signal sources in hyperspectral imagery [J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(3): 608-619.
[16] [16] N Acito, M Diani, G Corsini. Hyperspectral signal subspace identification in the presence of rare vectors and signal-dependent noise [J]. IEEE Transactions Geoscience and Remote Sensing, 2013, 51(1): 283-299.
[17] [17] C M Gittins. Detection and characterization of chemical vapor fugitive emissions by nonlinear optimal estimation: theory and simulation [J]. Appl Opt, 2009, 48(23): 4545-4561.
[19] [19] Alan Schaum. Methods of hyperspectral detection based on a single signature sample [J]. IEEE Sensors Journal, 2010, 10(3): 518-523.
[20] [20] Erin M O′Donnell, David W Messinger, Carl Salvaggio, et al.. Identification and detection of gaseous effluents from hyperspectral imagery using invariant algorithms [C]. SPIE, 2004, 5425: 573-582.
[21] [21] Joshua B Broadwater, Thomas S Spisz, Alison K Carr. Detection of gas plumes in cluttered environments using long-wave infrared hyperspectral sensors [J]. SPIE, 2008, 6954: 69540R.
Get Citation
Copy Citation Text
Cui Fangxiao, Fang Yonghua. Adaptive Detection for Pollutant Gases Based on Orthogonal Subspace Projection[J]. Acta Optica Sinica, 2014, 34(7): 730002
Category: Spectroscopy
Received: Jan. 1, 2014
Accepted: --
Published Online: May. 14, 2014
The Author Email: Fangxiao Cui (cfx2010ep@hotmail.com)