Abstract

A multispectral camera concept is presented. The concept is based on using a patterned filter in the focal plane, combined with scanning of the field of view. The filter layout has stripes of different bandpass filters extending orthogonally to the scan direction. The pattern of filter stripes is such that all bands are sampled multiple times, while minimizing the total duration of the sampling of a given scene point. As a consequence, the filter needs only a small part of the area of an image sensor. The remaining area can be used for conventional 2D imaging. A demonstrator camera has been built with six bands in the visible and near infrared, as well as a panchromatic 2D imaging capability. Image recording and reconstruction is demonstrated, but the quality of image reconstruction is expected to be a main challenge for systems based on this concept. An important advantage is that the camera can potentially be made very compact, and also low cost. It is shown that under assumptions that are not unreasonable, the proposed camera concept can be much smaller than a conventional imaging spectrometer. In principle, it can be smaller in volume by a factor on the order of several hundred while collecting the same amount of light per multispectral band. This makes the proposed camera concept very interesting for small airborne platforms and other applications requiring compact spectral imagers.

© 2014 Optical Society of America

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References

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  1. N. Tack, A. Lambrechts, P. Soussan, and L. Haspeslagh, “A compact high-speed and low-cost hyperspectral imager,” Proc. SPIE 8266, 82660Q (2012).
    [CrossRef]
  2. H. Saari, V.-V. Aallos, C. Holmlund, J. Mäkynen, B. Delauré, K. Nackaerts, and B. Michiels, “Novel hyperspectral imager for lightweight UAVs,” Proc. SPIE 7668, 766805 (2010).
    [CrossRef]
  3. M. Pisani and M. Zucco, “Compact imaging spectrometer combining Fourier transform spectroscopy with a Fabry–Perot interferometer,” Opt. Express 17, 8319–8331 (2009).
    [CrossRef]
  4. D. B. Cavanaugh, J. M. Lorenz, N. Unwin, M. Dombrowski, and P. Wilson, “VNIR hypersensor camera system,” Proc. SPIE 7457, 745700 (2009).
    [CrossRef]
  5. A. M. Mika, “Linear-wedge spectrometer,” Proc. SPIE 1298, 127–131 (1990).
    [CrossRef]
  6. P. Mouroulis, R. O. Green, and T. G. Chrien, “Design of pushbroom imaging spectrometers for optimum recovery of spectroscopic and spatial information,” Appl. Opt. 39, 2210–2220 (2000).
    [CrossRef]
  7. T. Skauli, “An upper-bound metric for characterizing spectral and spatial coregistration errors in spectral imaging,” Opt. Express 20, 918–933 (2012).
    [CrossRef]
  8. X. Sun, “Computerized component variable interference filter imaging spectrometer system method and apparatus,” U.S. patent6,211,906 (3April2001).
  9. J. Biesemans, B. Delaure, and B. Michiels, “Geometric referencing of multi-spectral data,” Patent application EP2513599 A1 (2012).
  10. T. Skauli, “Imaging unit,” Patent application NO20130382 (2013).
  11. I. Kåsen, A. Rødningsby, T. V. Haavardsholm, and T. Skauli, “Band selection for hyperspectral target-detection based on a multinormal mixture anomaly detection algorithm,” Proc. SPIE 6966, 696606 (2008).
    [CrossRef]
  12. W. J. Smith, Modern Optical Engineering, 3rd ed. (McGraw-Hill, 2000), p. 208.
  13. T. Skauli, R. Ingebrigtsen, and I. Kåsen, “Effect of light level and photon noise on hyperspectral target detection performance,” Proc. SPIE 6661, 66610D (2007).
    [CrossRef]
  14. R. Harley and A. Zisserman, Multiple View Geometry in Computer Vision, 2nd ed. (Cambridge University, 2003).

2012 (2)

N. Tack, A. Lambrechts, P. Soussan, and L. Haspeslagh, “A compact high-speed and low-cost hyperspectral imager,” Proc. SPIE 8266, 82660Q (2012).
[CrossRef]

T. Skauli, “An upper-bound metric for characterizing spectral and spatial coregistration errors in spectral imaging,” Opt. Express 20, 918–933 (2012).
[CrossRef]

2010 (1)

H. Saari, V.-V. Aallos, C. Holmlund, J. Mäkynen, B. Delauré, K. Nackaerts, and B. Michiels, “Novel hyperspectral imager for lightweight UAVs,” Proc. SPIE 7668, 766805 (2010).
[CrossRef]

2009 (2)

M. Pisani and M. Zucco, “Compact imaging spectrometer combining Fourier transform spectroscopy with a Fabry–Perot interferometer,” Opt. Express 17, 8319–8331 (2009).
[CrossRef]

D. B. Cavanaugh, J. M. Lorenz, N. Unwin, M. Dombrowski, and P. Wilson, “VNIR hypersensor camera system,” Proc. SPIE 7457, 745700 (2009).
[CrossRef]

2008 (1)

I. Kåsen, A. Rødningsby, T. V. Haavardsholm, and T. Skauli, “Band selection for hyperspectral target-detection based on a multinormal mixture anomaly detection algorithm,” Proc. SPIE 6966, 696606 (2008).
[CrossRef]

2007 (1)

T. Skauli, R. Ingebrigtsen, and I. Kåsen, “Effect of light level and photon noise on hyperspectral target detection performance,” Proc. SPIE 6661, 66610D (2007).
[CrossRef]

2000 (1)

1990 (1)

A. M. Mika, “Linear-wedge spectrometer,” Proc. SPIE 1298, 127–131 (1990).
[CrossRef]

Aallos, V.-V.

H. Saari, V.-V. Aallos, C. Holmlund, J. Mäkynen, B. Delauré, K. Nackaerts, and B. Michiels, “Novel hyperspectral imager for lightweight UAVs,” Proc. SPIE 7668, 766805 (2010).
[CrossRef]

Biesemans, J.

J. Biesemans, B. Delaure, and B. Michiels, “Geometric referencing of multi-spectral data,” Patent application EP2513599 A1 (2012).

Cavanaugh, D. B.

D. B. Cavanaugh, J. M. Lorenz, N. Unwin, M. Dombrowski, and P. Wilson, “VNIR hypersensor camera system,” Proc. SPIE 7457, 745700 (2009).
[CrossRef]

Chrien, T. G.

Delaure, B.

J. Biesemans, B. Delaure, and B. Michiels, “Geometric referencing of multi-spectral data,” Patent application EP2513599 A1 (2012).

Delauré, B.

H. Saari, V.-V. Aallos, C. Holmlund, J. Mäkynen, B. Delauré, K. Nackaerts, and B. Michiels, “Novel hyperspectral imager for lightweight UAVs,” Proc. SPIE 7668, 766805 (2010).
[CrossRef]

Dombrowski, M.

D. B. Cavanaugh, J. M. Lorenz, N. Unwin, M. Dombrowski, and P. Wilson, “VNIR hypersensor camera system,” Proc. SPIE 7457, 745700 (2009).
[CrossRef]

Green, R. O.

Haavardsholm, T. V.

I. Kåsen, A. Rødningsby, T. V. Haavardsholm, and T. Skauli, “Band selection for hyperspectral target-detection based on a multinormal mixture anomaly detection algorithm,” Proc. SPIE 6966, 696606 (2008).
[CrossRef]

Harley, R.

R. Harley and A. Zisserman, Multiple View Geometry in Computer Vision, 2nd ed. (Cambridge University, 2003).

Haspeslagh, L.

N. Tack, A. Lambrechts, P. Soussan, and L. Haspeslagh, “A compact high-speed and low-cost hyperspectral imager,” Proc. SPIE 8266, 82660Q (2012).
[CrossRef]

Holmlund, C.

H. Saari, V.-V. Aallos, C. Holmlund, J. Mäkynen, B. Delauré, K. Nackaerts, and B. Michiels, “Novel hyperspectral imager for lightweight UAVs,” Proc. SPIE 7668, 766805 (2010).
[CrossRef]

Ingebrigtsen, R.

T. Skauli, R. Ingebrigtsen, and I. Kåsen, “Effect of light level and photon noise on hyperspectral target detection performance,” Proc. SPIE 6661, 66610D (2007).
[CrossRef]

Kåsen, I.

I. Kåsen, A. Rødningsby, T. V. Haavardsholm, and T. Skauli, “Band selection for hyperspectral target-detection based on a multinormal mixture anomaly detection algorithm,” Proc. SPIE 6966, 696606 (2008).
[CrossRef]

T. Skauli, R. Ingebrigtsen, and I. Kåsen, “Effect of light level and photon noise on hyperspectral target detection performance,” Proc. SPIE 6661, 66610D (2007).
[CrossRef]

Lambrechts, A.

N. Tack, A. Lambrechts, P. Soussan, and L. Haspeslagh, “A compact high-speed and low-cost hyperspectral imager,” Proc. SPIE 8266, 82660Q (2012).
[CrossRef]

Lorenz, J. M.

D. B. Cavanaugh, J. M. Lorenz, N. Unwin, M. Dombrowski, and P. Wilson, “VNIR hypersensor camera system,” Proc. SPIE 7457, 745700 (2009).
[CrossRef]

Mäkynen, J.

H. Saari, V.-V. Aallos, C. Holmlund, J. Mäkynen, B. Delauré, K. Nackaerts, and B. Michiels, “Novel hyperspectral imager for lightweight UAVs,” Proc. SPIE 7668, 766805 (2010).
[CrossRef]

Michiels, B.

H. Saari, V.-V. Aallos, C. Holmlund, J. Mäkynen, B. Delauré, K. Nackaerts, and B. Michiels, “Novel hyperspectral imager for lightweight UAVs,” Proc. SPIE 7668, 766805 (2010).
[CrossRef]

J. Biesemans, B. Delaure, and B. Michiels, “Geometric referencing of multi-spectral data,” Patent application EP2513599 A1 (2012).

Mika, A. M.

A. M. Mika, “Linear-wedge spectrometer,” Proc. SPIE 1298, 127–131 (1990).
[CrossRef]

Mouroulis, P.

Nackaerts, K.

H. Saari, V.-V. Aallos, C. Holmlund, J. Mäkynen, B. Delauré, K. Nackaerts, and B. Michiels, “Novel hyperspectral imager for lightweight UAVs,” Proc. SPIE 7668, 766805 (2010).
[CrossRef]

Pisani, M.

Rødningsby, A.

I. Kåsen, A. Rødningsby, T. V. Haavardsholm, and T. Skauli, “Band selection for hyperspectral target-detection based on a multinormal mixture anomaly detection algorithm,” Proc. SPIE 6966, 696606 (2008).
[CrossRef]

Saari, H.

H. Saari, V.-V. Aallos, C. Holmlund, J. Mäkynen, B. Delauré, K. Nackaerts, and B. Michiels, “Novel hyperspectral imager for lightweight UAVs,” Proc. SPIE 7668, 766805 (2010).
[CrossRef]

Skauli, T.

T. Skauli, “An upper-bound metric for characterizing spectral and spatial coregistration errors in spectral imaging,” Opt. Express 20, 918–933 (2012).
[CrossRef]

I. Kåsen, A. Rødningsby, T. V. Haavardsholm, and T. Skauli, “Band selection for hyperspectral target-detection based on a multinormal mixture anomaly detection algorithm,” Proc. SPIE 6966, 696606 (2008).
[CrossRef]

T. Skauli, R. Ingebrigtsen, and I. Kåsen, “Effect of light level and photon noise on hyperspectral target detection performance,” Proc. SPIE 6661, 66610D (2007).
[CrossRef]

T. Skauli, “Imaging unit,” Patent application NO20130382 (2013).

Smith, W. J.

W. J. Smith, Modern Optical Engineering, 3rd ed. (McGraw-Hill, 2000), p. 208.

Soussan, P.

N. Tack, A. Lambrechts, P. Soussan, and L. Haspeslagh, “A compact high-speed and low-cost hyperspectral imager,” Proc. SPIE 8266, 82660Q (2012).
[CrossRef]

Sun, X.

X. Sun, “Computerized component variable interference filter imaging spectrometer system method and apparatus,” U.S. patent6,211,906 (3April2001).

Tack, N.

N. Tack, A. Lambrechts, P. Soussan, and L. Haspeslagh, “A compact high-speed and low-cost hyperspectral imager,” Proc. SPIE 8266, 82660Q (2012).
[CrossRef]

Unwin, N.

D. B. Cavanaugh, J. M. Lorenz, N. Unwin, M. Dombrowski, and P. Wilson, “VNIR hypersensor camera system,” Proc. SPIE 7457, 745700 (2009).
[CrossRef]

Wilson, P.

D. B. Cavanaugh, J. M. Lorenz, N. Unwin, M. Dombrowski, and P. Wilson, “VNIR hypersensor camera system,” Proc. SPIE 7457, 745700 (2009).
[CrossRef]

Zisserman, A.

R. Harley and A. Zisserman, Multiple View Geometry in Computer Vision, 2nd ed. (Cambridge University, 2003).

Zucco, M.

Appl. Opt. (1)

Opt. Express (2)

Proc. SPIE (6)

D. B. Cavanaugh, J. M. Lorenz, N. Unwin, M. Dombrowski, and P. Wilson, “VNIR hypersensor camera system,” Proc. SPIE 7457, 745700 (2009).
[CrossRef]

A. M. Mika, “Linear-wedge spectrometer,” Proc. SPIE 1298, 127–131 (1990).
[CrossRef]

N. Tack, A. Lambrechts, P. Soussan, and L. Haspeslagh, “A compact high-speed and low-cost hyperspectral imager,” Proc. SPIE 8266, 82660Q (2012).
[CrossRef]

H. Saari, V.-V. Aallos, C. Holmlund, J. Mäkynen, B. Delauré, K. Nackaerts, and B. Michiels, “Novel hyperspectral imager for lightweight UAVs,” Proc. SPIE 7668, 766805 (2010).
[CrossRef]

I. Kåsen, A. Rødningsby, T. V. Haavardsholm, and T. Skauli, “Band selection for hyperspectral target-detection based on a multinormal mixture anomaly detection algorithm,” Proc. SPIE 6966, 696606 (2008).
[CrossRef]

T. Skauli, R. Ingebrigtsen, and I. Kåsen, “Effect of light level and photon noise on hyperspectral target detection performance,” Proc. SPIE 6661, 66610D (2007).
[CrossRef]

Other (5)

R. Harley and A. Zisserman, Multiple View Geometry in Computer Vision, 2nd ed. (Cambridge University, 2003).

W. J. Smith, Modern Optical Engineering, 3rd ed. (McGraw-Hill, 2000), p. 208.

X. Sun, “Computerized component variable interference filter imaging spectrometer system method and apparatus,” U.S. patent6,211,906 (3April2001).

J. Biesemans, B. Delaure, and B. Michiels, “Geometric referencing of multi-spectral data,” Patent application EP2513599 A1 (2012).

T. Skauli, “Imaging unit,” Patent application NO20130382 (2013).

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Figures (10)

Fig. 1.
Fig. 1.

(a) Sketch of the optical layout of the camera. A patterned filter is placed on the image sensor in the focal plane of an objective lens. (b) Sketch of the filter pattern. The filter has multiple regions with different bandpass characteristics laid out in stripes across the image sensor. Each spectral band is repeated multiple times across the filter. A large part of the image sensor is left unfiltered, for use in conventional imaging.

Fig. 2.
Fig. 2.

Parallax effect on the recorded spectrum. Top: a camera that makes a single sample in each band. The arrow indicates the motion of the camera in an airborne imaging application. Red and blue lines indicate lines of sight for the leading and trailing band for two positions along the scan motion, with other bands in between as suggested by the “spectrum” bar. The green line indicates ground with a point P to be observed, and a building B causes parallax effects, see text. Bottom: a camera that samples each band twice can overcome the parallax effect by recording missing data for point P at a later position in the scan.

Fig. 3.
Fig. 3.

Chosen spectral bands (top) in comparison with VNIR bands of various earth observation satellites.

Fig. 4.
Fig. 4.

Layout of the filter for the demonstrator system. Stripes of six different bandpass filters extend across the image sensor, orthogonal to the nominal scan direction. The set of filters is repeated four times. The neighboring areas of the filter substrate are AR coated.

Fig. 5.
Fig. 5.

Measured transmission spectra of the bandpass filters at normal incidence.

Fig. 6.
Fig. 6.

Sketch of the focal plane assembly. The filter is placed close to the image sensor. The filter substrate extends across the entire sensor, with only AR coating on the unfiltered parts.

Fig. 7.
Fig. 7.

Front view of the assembled camera without lens. The patterned filter covers the right part of the image sensor. A metal fixture holds the filter substrate in place.

Fig. 8.
Fig. 8.

Measured transmission spectrum of the filter for the green band at 0 deg (solid), 12 deg (long dash), and 25 deg (short dash) angle of incidence.

Fig. 9.
Fig. 9.

Relative shift of spectral features of the filter for varying angle of incidence, according to the approximation (Eq. 1), assuming an effective refractive index ne=1.7.

Fig. 10.
Fig. 10.

Example images recorded in the lab. The scene consists of a real and an artificial plant with a checkerboard background. (a) Two superimposed raw images showing the point correspondences used to estimate the transformation between them. The stripe pattern of filters and shadow masks is seen on the left. (Shadowed areas at the top and bottom are due to the clamp holding the filter.) (b) Reconstructed RGB image after averaging multiple spectral samples in each pixel. (c) Result of maximum likelihood spectral classification.

Equations (2)

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Δλλ1(sinθne)21θ22ne2,
ΔS1=Δλλ2λ1L1L2L1S1.

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