Abstract

We analyze 7258 global spectral irradiance functions over 0.4–2.2 µm that were acquired over a wide range of conditions at Boulder, Colorado, during the summer of 1997. We show that low-dimensional linear models can be used to capture the variability in these spectra over both the visible and the 0.4–2.2 µm spectral ranges. Using a linear model, we compare the Boulder data with the previous study of Judd et al. [J. Opt. Soc. Am. 54, 1031 (1964)] over the visible wavelengths. We also examine the agreement of the Boulder data with a spectral database generated by using the MODTRAN 4.0 radiative transfer code. We use a database of 223 minerals to consider the effect of the spectral variability in the global spectral irradiance functions on hyperspectral material identification. We show that the 223 minerals can be discriminated accurately over the variability in the Boulder data with subspace projection techniques.

© 2003 Optical Society of America

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    [CrossRef]

2001 (2)

2000 (1)

B. Kránicz, J. Schanda, “Reevaluation of daylightspectral distributions,” Color Res. Appl. 25, 250–259 (2000).
[CrossRef]

1999 (1)

G. Healey, D. Slater, “Models and methods for automated material identification in hyperspectral imagery acquired under unknown illumination and atmospheric conditions,” IEEE Trans. Geosci. Remote Sens. 37, 2706–2717 (1999).
[CrossRef]

1998 (3)

1997 (2)

J. Romero, A. Garcı́a-Beltrán, J. Hernández-Andrés, “Linear bases for representation of natural and artificial illuminants,” J. Opt. Soc. Am. A 14, 1007–1014 (1997).
[CrossRef]

R. Resmini, M. Kappus, W. Aldrich, J. Harsanyi, M. Anderson, “Mineral mapping with Hyperspectral Digital Imagery Collection Experiment (HYDICE) sensor data at Cuprite, Nevada, USA,” Int. J. Remote Sens. 18, 1553–1570 (1997).
[CrossRef]

1994 (1)

M. Vrhel, R. Gershon, L. Iwan, “Measurement and analysis of object reflectance spectra,” Color Res. Appl. 19, 4–9 (1994).

1993 (2)

G. Vane, R. Green, T. Chrien, H. Enmark, E. Hansen, W. Porter, “The airborne visible/infrared imaging spectrometer (AVIRIS),” Remote Sens. Environ. 44, 127–143 (1993).
[CrossRef]

M. D’Zmura, G. Iverson, “Color constancy. I. Basic theory of two-stage linear recovery of spectral descriptions for lights and surfaces,” J. Opt. Soc. Am. A 10, 2148–2165 (1993).
[CrossRef]

1992 (3)

L. Rothman, “The HITRAN molecular database: editions of 1991 and 1992,” J. Quant. Spectrosc. Radiat. Transf. 48, 469–507 (1992).
[CrossRef]

S. Clough, M. Iacono, J.-L. Moncet, “Line-by-line calculations of atmospheric fluxes and cooling rates: application to water vapor,” J. Geophys. Res. 97, 15761–15785 (1992).
[CrossRef]

D. Marimont, B. Wandell, “Linear models of surfaceand illuminant spectra,” J. Opt. Soc. Am. A 9, 1905–1913 (1992).
[CrossRef] [PubMed]

1990 (2)

F. Kruse, K. Kierein-Young, J. Boardman, “Mineral mapping at Cuprite, Nevada, with a 63-channel imaging spectrometer,” Photogramm. Eng. Remote Sens. 56, 83–92 (1990).

J. Ho, B. Funt, M. Drew, “Separating a color signal into illumination and surface reflectance components: theory and applications,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 966–977 (1990).
[CrossRef]

1989 (1)

1986 (2)

1978 (1)

1968 (1)

1966 (2)

1965 (1)

1964 (2)

Aldrich, W.

R. Resmini, M. Kappus, W. Aldrich, J. Harsanyi, M. Anderson, “Mineral mapping with Hyperspectral Digital Imagery Collection Experiment (HYDICE) sensor data at Cuprite, Nevada, USA,” Int. J. Remote Sens. 18, 1553–1570 (1997).
[CrossRef]

Anderson, M.

R. Resmini, M. Kappus, W. Aldrich, J. Harsanyi, M. Anderson, “Mineral mapping with Hyperspectral Digital Imagery Collection Experiment (HYDICE) sensor data at Cuprite, Nevada, USA,” Int. J. Remote Sens. 18, 1553–1570 (1997).
[CrossRef]

Basedow, R.

L. Rickard, R. Basedow, E. Zalewski, P. Silverglate, M. Landers, “HYDICE: an airborne system for hyperspectral imaging,” in Imaging Spectrometry of the Terrestrial Environment, G. Vane, ed., Proc. SPIE1937, 173–179 (1993).
[CrossRef]

Beaven, S.

C. Simi, S. Beaven, E. Winter, C. LaSota, J. Parish, R. Dixon, “Night vision imaging spectrometer (NVIS) performance parameters and their impact on various detection algorithms,” in Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, S. Shen, M. Descovr, eds., Proc. SPIE4049, 218–229 (2000).
[CrossRef]

Benites, L.

Berk, A.

A. Berk, L. Bernstein, D. Robertson, “MODTRAN: a moderate resolution model for LOWTRAN 7,” (Geophysics Laboratory, Bedford, Mass., 1989).

Bernstein, L.

A. Berk, L. Bernstein, D. Robertson, “MODTRAN: a moderate resolution model for LOWTRAN 7,” (Geophysics Laboratory, Bedford, Mass., 1989).

Boardman, J.

F. Kruse, K. Kierein-Young, J. Boardman, “Mineral mapping at Cuprite, Nevada, with a 63-channel imaging spectrometer,” Photogramm. Eng. Remote Sens. 56, 83–92 (1990).

Boshoff, M.

Calvin, W.

R. Clark, G. Swayze, A. Gallagher, T. King, W. Calvin, “The U.S. geological survey, digital spectral library: version 1: 0.2 to 3.0 microns,” (U.S. Geological Survey, Reston, Va., 1993).

Chrien, T.

G. Vane, R. Green, T. Chrien, H. Enmark, E. Hansen, W. Porter, “The airborne visible/infrared imaging spectrometer (AVIRIS),” Remote Sens. Environ. 44, 127–143 (1993).
[CrossRef]

Clark, R.

R. Clark, G. Swayze, A. Gallagher, T. King, W. Calvin, “The U.S. geological survey, digital spectral library: version 1: 0.2 to 3.0 microns,” (U.S. Geological Survey, Reston, Va., 1993).

Clough, S.

S. Clough, M. Iacono, J.-L. Moncet, “Line-by-line calculations of atmospheric fluxes and cooling rates: application to water vapor,” J. Geophys. Res. 97, 15761–15785 (1992).
[CrossRef]

Cohen, J.

J. Cohen, “Dependency of the spectral reflectance curves of the Munsell color chips,” Psychon. Sci. 1, 369–370 (1964).
[CrossRef]

D’Zmura, M.

Das, S.

Dixon, E.

Dixon, R.

C. Simi, S. Beaven, E. Winter, C. LaSota, J. Parish, R. Dixon, “Night vision imaging spectrometer (NVIS) performance parameters and their impact on various detection algorithms,” in Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, S. Shen, M. Descovr, eds., Proc. SPIE4049, 218–229 (2000).
[CrossRef]

Drew, M.

J. Ho, B. Funt, M. Drew, “Separating a color signal into illumination and surface reflectance components: theory and applications,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 966–977 (1990).
[CrossRef]

DuToit, A.

Enmark, H.

G. Vane, R. Green, T. Chrien, H. Enmark, E. Hansen, W. Porter, “The airborne visible/infrared imaging spectrometer (AVIRIS),” Remote Sens. Environ. 44, 127–143 (1993).
[CrossRef]

Funt, B.

J. Ho, B. Funt, M. Drew, “Separating a color signal into illumination and surface reflectance components: theory and applications,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 966–977 (1990).
[CrossRef]

Gallagher, A.

R. Clark, G. Swayze, A. Gallagher, T. King, W. Calvin, “The U.S. geological survey, digital spectral library: version 1: 0.2 to 3.0 microns,” (U.S. Geological Survey, Reston, Va., 1993).

Garci´a-Beltrán, A.

Gershon, R.

M. Vrhel, R. Gershon, L. Iwan, “Measurement and analysis of object reflectance spectra,” Color Res. Appl. 19, 4–9 (1994).

Golub, G.

G. Golub, C. van Loan, Matrix Computations (Johns Hopkins U. Press, Baltimore, Md., 1983).

Green, R.

G. Vane, R. Green, T. Chrien, H. Enmark, E. Hansen, W. Porter, “The airborne visible/infrared imaging spectrometer (AVIRIS),” Remote Sens. Environ. 44, 127–143 (1993).
[CrossRef]

Hallikainen, J.

Hansen, E.

G. Vane, R. Green, T. Chrien, H. Enmark, E. Hansen, W. Porter, “The airborne visible/infrared imaging spectrometer (AVIRIS),” Remote Sens. Environ. 44, 127–143 (1993).
[CrossRef]

Harsanyi, J.

R. Resmini, M. Kappus, W. Aldrich, J. Harsanyi, M. Anderson, “Mineral mapping with Hyperspectral Digital Imagery Collection Experiment (HYDICE) sensor data at Cuprite, Nevada, USA,” Int. J. Remote Sens. 18, 1553–1570 (1997).
[CrossRef]

Healey, G.

G. Healey, D. Slater, “Models and methods for automated material identification in hyperspectral imagery acquired under unknown illumination and atmospheric conditions,” IEEE Trans. Geosci. Remote Sens. 37, 2706–2717 (1999).
[CrossRef]

G. Healey, L. Benites, “Linear models for spectral reflectance functions over the mid-wave and long-wave infrared,” J. Opt. Soc. Am. A 15, 2216–2227 (1998).
[CrossRef]

D. Slater, G. Healey, “Analyzing the spectral dimension-ality of outdoor visible and near-infrared illumination functions,” J. Opt. Soc. Am. A 15, 2913–2920 (1998).
[CrossRef]

G. Healey, Q.-T. Luong, “Color in computer vision: recent progress,” in Handbook of Pattern Recognition and Computer Vision, C. Chen, L. Pau, P. Wang, eds. (World Scientific, Singapore, 1999), pp. 283–312.

Hernández-Andrés, J.

Ho, J.

J. Ho, B. Funt, M. Drew, “Separating a color signal into illumination and surface reflectance components: theory and applications,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 966–977 (1990).
[CrossRef]

Iacono, M.

S. Clough, M. Iacono, J.-L. Moncet, “Line-by-line calculations of atmospheric fluxes and cooling rates: application to water vapor,” J. Geophys. Res. 97, 15761–15785 (1992).
[CrossRef]

Iverson, G.

Iwan, L.

M. Vrhel, R. Gershon, L. Iwan, “Measurement and analysis of object reflectance spectra,” Color Res. Appl. 19, 4–9 (1994).

Jaaskelainen, T.

Judd, D.

Kappus, M.

R. Resmini, M. Kappus, W. Aldrich, J. Harsanyi, M. Anderson, “Mineral mapping with Hyperspectral Digital Imagery Collection Experiment (HYDICE) sensor data at Cuprite, Nevada, USA,” Int. J. Remote Sens. 18, 1553–1570 (1997).
[CrossRef]

Kierein-Young, K.

F. Kruse, K. Kierein-Young, J. Boardman, “Mineral mapping at Cuprite, Nevada, with a 63-channel imaging spectrometer,” Photogramm. Eng. Remote Sens. 56, 83–92 (1990).

King, T.

R. Clark, G. Swayze, A. Gallagher, T. King, W. Calvin, “The U.S. geological survey, digital spectral library: version 1: 0.2 to 3.0 microns,” (U.S. Geological Survey, Reston, Va., 1993).

Kok, C.

Kránicz, B.

B. Kránicz, J. Schanda, “Reevaluation of daylightspectral distributions,” Color Res. Appl. 25, 250–259 (2000).
[CrossRef]

Kruse, F.

F. Kruse, K. Kierein-Young, J. Boardman, “Mineral mapping at Cuprite, Nevada, with a 63-channel imaging spectrometer,” Photogramm. Eng. Remote Sens. 56, 83–92 (1990).

Landers, M.

L. Rickard, R. Basedow, E. Zalewski, P. Silverglate, M. Landers, “HYDICE: an airborne system for hyperspectral imaging,” in Imaging Spectrometry of the Terrestrial Environment, G. Vane, ed., Proc. SPIE1937, 173–179 (1993).
[CrossRef]

LaSota, C.

C. Simi, S. Beaven, E. Winter, C. LaSota, J. Parish, R. Dixon, “Night vision imaging spectrometer (NVIS) performance parameters and their impact on various detection algorithms,” in Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, S. Shen, M. Descovr, eds., Proc. SPIE4049, 218–229 (2000).
[CrossRef]

Luong, Q.-T.

G. Healey, Q.-T. Luong, “Color in computer vision: recent progress,” in Handbook of Pattern Recognition and Computer Vision, C. Chen, L. Pau, P. Wang, eds. (World Scientific, Singapore, 1999), pp. 283–312.

MacAdam, D.

Maloney, L.

Marimont, D.

Moncet, J.-L.

S. Clough, M. Iacono, J.-L. Moncet, “Line-by-line calculations of atmospheric fluxes and cooling rates: application to water vapor,” J. Geophys. Res. 97, 15761–15785 (1992).
[CrossRef]

Nieves, J.

Parish, J.

C. Simi, S. Beaven, E. Winter, C. LaSota, J. Parish, R. Dixon, “Night vision imaging spectrometer (NVIS) performance parameters and their impact on various detection algorithms,” in Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, S. Shen, M. Descovr, eds., Proc. SPIE4049, 218–229 (2000).
[CrossRef]

Parkkinen, J.

Porter, W.

G. Vane, R. Green, T. Chrien, H. Enmark, E. Hansen, W. Porter, “The airborne visible/infrared imaging spectrometer (AVIRIS),” Remote Sens. Environ. 44, 127–143 (1993).
[CrossRef]

Resmini, R.

R. Resmini, M. Kappus, W. Aldrich, J. Harsanyi, M. Anderson, “Mineral mapping with Hyperspectral Digital Imagery Collection Experiment (HYDICE) sensor data at Cuprite, Nevada, USA,” Int. J. Remote Sens. 18, 1553–1570 (1997).
[CrossRef]

Rickard, L.

L. Rickard, R. Basedow, E. Zalewski, P. Silverglate, M. Landers, “HYDICE: an airborne system for hyperspectral imaging,” in Imaging Spectrometry of the Terrestrial Environment, G. Vane, ed., Proc. SPIE1937, 173–179 (1993).
[CrossRef]

Robertson, D.

A. Berk, L. Bernstein, D. Robertson, “MODTRAN: a moderate resolution model for LOWTRAN 7,” (Geophysics Laboratory, Bedford, Mass., 1989).

Romero, J.

Rothman, L.

L. Rothman, “The HITRAN molecular database: editions of 1991 and 1992,” J. Quant. Spectrosc. Radiat. Transf. 48, 469–507 (1992).
[CrossRef]

Sastri, V.

Schanda, J.

B. Kránicz, J. Schanda, “Reevaluation of daylightspectral distributions,” Color Res. Appl. 25, 250–259 (2000).
[CrossRef]

Schott, J.

J. Schott, Remote Sensing: The Image Chain Approach (Oxford U. Press, New York, 1997).

Silverglate, P.

L. Rickard, R. Basedow, E. Zalewski, P. Silverglate, M. Landers, “HYDICE: an airborne system for hyperspectral imaging,” in Imaging Spectrometry of the Terrestrial Environment, G. Vane, ed., Proc. SPIE1937, 173–179 (1993).
[CrossRef]

Simi, C.

C. Simi, S. Beaven, E. Winter, C. LaSota, J. Parish, R. Dixon, “Night vision imaging spectrometer (NVIS) performance parameters and their impact on various detection algorithms,” in Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, S. Shen, M. Descovr, eds., Proc. SPIE4049, 218–229 (2000).
[CrossRef]

Slater, D.

G. Healey, D. Slater, “Models and methods for automated material identification in hyperspectral imagery acquired under unknown illumination and atmospheric conditions,” IEEE Trans. Geosci. Remote Sens. 37, 2706–2717 (1999).
[CrossRef]

D. Slater, G. Healey, “Analyzing the spectral dimension-ality of outdoor visible and near-infrared illumination functions,” J. Opt. Soc. Am. A 15, 2913–2920 (1998).
[CrossRef]

Stiles, W.

G. Wyszecki, W. Stiles, Color Science: Concepts and Methods, Quantitative Data and Formulae (Wiley, New York, 1982).

Swayze, G.

R. Clark, G. Swayze, A. Gallagher, T. King, W. Calvin, “The U.S. geological survey, digital spectral library: version 1: 0.2 to 3.0 microns,” (U.S. Geological Survey, Reston, Va., 1993).

van Loan, C.

G. Golub, C. van Loan, Matrix Computations (Johns Hopkins U. Press, Baltimore, Md., 1983).

Vane, G.

G. Vane, R. Green, T. Chrien, H. Enmark, E. Hansen, W. Porter, “The airborne visible/infrared imaging spectrometer (AVIRIS),” Remote Sens. Environ. 44, 127–143 (1993).
[CrossRef]

Vrhel, M.

M. Vrhel, R. Gershon, L. Iwan, “Measurement and analysis of object reflectance spectra,” Color Res. Appl. 19, 4–9 (1994).

Wandell, B.

Winch, G.

Winter, E.

C. Simi, S. Beaven, E. Winter, C. LaSota, J. Parish, R. Dixon, “Night vision imaging spectrometer (NVIS) performance parameters and their impact on various detection algorithms,” in Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, S. Shen, M. Descovr, eds., Proc. SPIE4049, 218–229 (2000).
[CrossRef]

Wyszecki, G.

Zalewski, E.

L. Rickard, R. Basedow, E. Zalewski, P. Silverglate, M. Landers, “HYDICE: an airborne system for hyperspectral imaging,” in Imaging Spectrometry of the Terrestrial Environment, G. Vane, ed., Proc. SPIE1937, 173–179 (1993).
[CrossRef]

Appl. Opt. (1)

Color Res. Appl. (2)

M. Vrhel, R. Gershon, L. Iwan, “Measurement and analysis of object reflectance spectra,” Color Res. Appl. 19, 4–9 (1994).

B. Kránicz, J. Schanda, “Reevaluation of daylightspectral distributions,” Color Res. Appl. 25, 250–259 (2000).
[CrossRef]

IEEE Trans. Geosci. Remote Sens. (1)

G. Healey, D. Slater, “Models and methods for automated material identification in hyperspectral imagery acquired under unknown illumination and atmospheric conditions,” IEEE Trans. Geosci. Remote Sens. 37, 2706–2717 (1999).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

J. Ho, B. Funt, M. Drew, “Separating a color signal into illumination and surface reflectance components: theory and applications,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 966–977 (1990).
[CrossRef]

Int. J. Remote Sens. (1)

R. Resmini, M. Kappus, W. Aldrich, J. Harsanyi, M. Anderson, “Mineral mapping with Hyperspectral Digital Imagery Collection Experiment (HYDICE) sensor data at Cuprite, Nevada, USA,” Int. J. Remote Sens. 18, 1553–1570 (1997).
[CrossRef]

J. Geophys. Res. (1)

S. Clough, M. Iacono, J.-L. Moncet, “Line-by-line calculations of atmospheric fluxes and cooling rates: application to water vapor,” J. Geophys. Res. 97, 15761–15785 (1992).
[CrossRef]

J. Opt. Soc. Am. (6)

J. Opt. Soc. Am. A (10)

J. Hernández-Andrés, J. Romero, “Colorimetric and spectroradiometric characteristics of narrow-field-of-view clear skylight in Granada, Spain,” J. Opt. Soc. Am. A 18, 412–420 (2001).
[CrossRef]

D. Marimont, B. Wandell, “Linear models of surfaceand illuminant spectra,” J. Opt. Soc. Am. A 9, 1905–1913 (1992).
[CrossRef] [PubMed]

J. Hernández-Andrés, J. Romero, J. Nieves, “Color and spectral analysis of daylight in southern Europe,” J. Opt. Soc. Am. A 18, 1325–1335 (2001).
[CrossRef]

G. Healey, L. Benites, “Linear models for spectral reflectance functions over the mid-wave and long-wave infrared,” J. Opt. Soc. Am. A 15, 2216–2227 (1998).
[CrossRef]

D. Slater, G. Healey, “Analyzing the spectral dimension-ality of outdoor visible and near-infrared illumination functions,” J. Opt. Soc. Am. A 15, 2913–2920 (1998).
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Figures (22)

Fig. 1
Fig. 1

Chromaticities of Boulder daylight compared with Planckian locus and the CIE daylight locus.

Fig. 2
Fig. 2

Average normalized error over 0.35–0.7 µm.

Fig. 3
Fig. 3

Fits of four illumination functions with one basis vector (0.35–0.7 µm).

Fig. 4
Fig. 4

Fits of four illumination functions with three basis vectors (0.35–0.7 µm).

Fig. 5
Fig. 5

Fits of four illumination functions with six basis vectors (0.35–0.7 µm).

Fig. 6
Fig. 6

Average normalized error over 0.4–2.2 µm.

Fig. 7
Fig. 7

Fits of four illumination functions with one basis vector (0.4–2.2 µm).

Fig. 8
Fig. 8

Fits of four illumination functions with three basis vectors (0.4–2.2 µm).

Fig. 9
Fig. 9

Fits of four illumination functions with eight basis vectors (0.4–2.2 µm).

Fig. 10
Fig. 10

Fit for Judd mean vector E¯(λ) with five Boulder basis vectors.

Fig. 11
Fig. 11

Fit for Judd vector V1(λ) with five Boulder basis vectors.

Fig. 12
Fig. 12

Fit for Judd vector V2(λ) with five Boulder basis vectors.

Fig. 13
Fig. 13

Fit for Judd vector V3(λ) with five Boulder basis vectors.

Fig. 14
Fig. 14

Fit for Judd vector V4(λ) with five Boulder basis vectors.

Fig. 15
Fig. 15

Fit for Boulder basis vector l1 with five Judd vectors.

Fig. 16
Fig. 16

Fit for Boulder basis vector l2 with five Judd vectors.

Fig. 17
Fig. 17

Fit for Boulder basis vector l3 with five Judd vectors.

Fig. 18
Fig. 18

Fit for Boulder basis vector l1 with five MODTRAN basis vectors.

Fig. 19
Fig. 19

Fit for Boulder basis vector l2 with five MODTRAN basis vectors.

Fig. 20
Fig. 20

Fit for Boulder basis vector l3 with five MODTRAN basis vectors.

Fig. 21
Fig. 21

Examples of mineral reflectance similarity.

Fig. 22
Fig. 22

Identification error rate for the 1,618,534 reflected radiance spectra of 223 minerals.

Tables (1)

Tables Icon

Table 1 Range of MODTRAN parameters

Equations (7)

Equations on this page are rendered with MathJax. Learn more.

L(λ)=Td(θo, ϕo, λ)Eo(λ)cos(θo)+ϕ=02πθ=0π/2Es(θ, ϕ, λ)cos(θ)sin(θ)dθdϕ,
Lij=1Nσijlj,
Ei=Li-j=1Nσijlj2.
ET=i=1MEi,
ET¯=ETi=1MLi2.
k=17258Ri,k-j=1Nσijkbij2
Di=R-j=1N(R·bij)bij2.

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