Method to analyze remotely sensed spectral data

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Reexamination Certificate

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Reexamination Certificate

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07491944

ABSTRACT:
A fast and rigorous multivariate curve resolution (MCR) algorithm is applied to remotely sensed spectral data. The algorithm is applicable in the solar-reflective spectral region, comprising the visible to the shortwave infrared (ranging from approximately 0.4 to 2.5 μm), midwave infrared, and thermal emission spectral region, comprising the thermal infrared (ranging from approximately 8 to 15 μm). For example, employing minimal a priori knowledge, notably non-negativity constraints on the extracted endmember profiles and a constant abundance constraint for the atmospheric upwelling component, MCR can be used to successfully compensate thermal infrared hyperspectral images for atmospheric upwelling and, thereby, transmittance effects. Further, MCR can accurately estimate the relative spectral absorption coefficients and thermal contrast distribution of a gas plume component near the minimum detectable quantity.

REFERENCES:
patent: 6584413 (2003-06-01), Keenan et al.
patent: 6675106 (2004-01-01), Keenan et al.
patent: 7283684 (2007-10-01), Keenan
Tauler et al.,Multivariate Curve Resolution Applied to Spectral Data from Multiple Runs of an Industrial Process, 1993,Anal. Chem.,65,2040.
Keenan et al., Accounting for Poisson noise in the multivariate analysis of Tof-SIMS spectrum images,2004,Surf. Interface Anal.,36,203-212.
Chris L. Stork et al, “Multivariate curve resolution for the analysis of remotely sensed thermal infrared hyperspectral images”, Proceedings of SPIE vol. 5546 (2004), pp. 271-284.
Nirmal Keshava and John F. Mustard, “Spectral Unmixing”, IEEE Signal Processing Magazine, Jan. 2002 pp. 44-57.
S. J. Young, “Detection and Quantification of Gases in Industrial-Stack Plumes Using Thermal-Infrared Hyperspectral Imaging,” the Aerospace Corporation, Aerospace Report No. ATR-2002(8407)-1, Feb. 10, 2002.
Christopher C. Funk et al, “Clustering to Improve Matched Filter Detection of Weak Gas Plumes in Hyperspectral Thermal Imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 39, No. 7, Jul. 2001 pp. 1410-1420.
Roma Tauler and Bruce Kowalski, “Multivariate Curve Resolution Applied to Spectral Data from Multiple Runs of an Industrial Process”, American Chemical Society, vol. 65, No. 15, (1993) pp. 2040-2047.
Rasmus Bro and Sijmen De Jongl, “A Fast Non-Negativity-Constrained Least Squares Algorithm,” Journal of Chemometrics, vol. 11, 393-401 (1997).
Mark H. Van Benthem et al, “Application of equality constraints on variables during alternating least squares procedures,” Journal of Chemometrics, 2002, vol. 16 pp. 613-622.
Michael R. Keenan, “Methods for Spectral Image Analysis by Exploiting Spatial Simplicity,” U.S. Appl. No. 11/233,223, filed Sep. 22, 2005.
Henry F. Kaiser, “The Varimax Criterion for Analytic Rotation in Factor Analysis,” Psychometrika, vol. 23 No. 3, Sep. 1958 pp. 187-200.
S. W. Sharpe, et al, Creation of 0.10 cm−1Resolution, Quantitative, Infrared Spectral Libraries for Gas Samples, Proceedings of SPIE vol. 4577 (2002) pp. 12-24.
Degui Gu et al, “Autonomous Atmospheric Compensation (AAC) of High Resolution Hyperspectral Thermal Infrared Remote-Sensing Imagery”, IEEE Transactions on Geoscience and Remote Sensing, vol. 38, No. 6, Nov. 2000, pp. 2557-2570.

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