Abstract

This paper reports the design and construction of a low-cost, multispectral imaging system using a single, large format CCD and an array of 18 individual lenses coupled to individual spectral filters. The system allows the simultaneous acquisition of 18 subimages, each with potentially different optical information. The subimages are combined to create a composite image, highlighting the desired spectral information. Because all the subimages are acquired simultaneously, the composite image shows no motion artifact. Although the present configuration uses 17 narrow bandpass optical filters to obtain multispectral information from a scene, the system is designed to be a general purpose, multiaperture platform, easily reconfigured for other multiaperture imaging modes.

© 2008 Optical Society of America

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  1. J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, “Classification of hyperspectral data from urban areas based on extended morphological profiles,” IEEE Trans. Geosci. Remote Sens. 43, 480-491 (2005).
    [Crossref]
  2. D. G. Goodenough, J. Pearlman, H. Chen, A. Dyk, T. Han, J. Li, J. Miller, and K. O. Niemann, “Forest information from hyperspectral sensing,” in IEEE Proceedings on Geoscience and Remote Sensing (IEEE, 2004), Vol. 4, pp. 2585-2589.
  3. T. Vo-Dinh, “A hyperspectral imaging system for in vivo optical diagnostics,” IEEE Eng. Med. Biol. Magazine 23(5), 40-49 (2004).
    [Crossref]
  4. J. A. Timlin, M. B. Sinclair, D. M. Haaland, J. Martinez, M. Manginell, S. M. Brozk, J. F. Guzowski, and M. Werner-Washburne, “Hyperspectral imaging of biological targets: the difference a high resolution spectral dimension and multivariate analysis can make,” in IEEE Symposium on Biomedical Imaging (IEEE, 2004), Vol. 2, pp. 1529-1532.
  5. C.-I. Chang, E. Sun, and M. L. G. Althouse, “An unsupervised interference rejection approach to target detection and classification for hyperspectral imagery,” Opt. Eng. 37, 735-743(1998).
    [Crossref]
  6. H. Ren, Q. Du, J. Wang, C.-I. Chang, J. O. Jensen, and J. L. Jensen, “Automatic target recognition for hyperspectral imagery using high-order statistics,” IEEE Trans. Aerosp. Electron. Syst. 42, 1372-1385 (2006).
    [Crossref]
  7. G. Themelis, J. S. Yoo, and V. Ntziachristos, “Multispectral imaging using multiple-bandpass filters,” Opt. Lett. 33, 1023-1025 (2008).
    [Crossref] [PubMed]
  8. E. M. Winter, “Methods for determining best multispectral bands using hyperspectral data,” in IEEE Aerospace Conference (IEEE, 2007), pp. 1-6.
    [Crossref]
  9. E. Hecht, “Familiar aspects of the interaction of light and matter,” in Optics, 4th ed. (Addison-Wesley, 2002), pp. 131-136.
  10. C. Boyce, A. Ross, M. Monaco, L. Hornak, and X. Li, “Multispectral iris analysis: a preliminary study,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2006), pp. 51-51.
  11. M. J. Wabomba, Y. Sulub, and G. W. Small, “Remote detection of volatile organic compounds by passive multispectral infrared imaging measurements,” Appl. Spectrosc. 61, 349-358(2007).
    [Crossref] [PubMed]
  12. A. Weisberg, M. Najarian, B. Borowski, J. Lisowski, and B. Miller, “Spectral angle automatic cluster routine (SAALT): an unsupervised multispectral clustering algorithm,” in IEEE Proceedings, Aerospace Conference 1999 (IEEE, 1999), pp. 307-317.
  13. J. R. Mansfield, M. G. Sowa, J. R. Payette, B. Abdulrauf, M. F. Stranc, and H. H. Mantsch, “Tissue viability by multispectral near infrared imaging: a Fuzzy C-Mean Clustering Analysis,” IEEE Trans. Med. Imaging 17, 1011-1018 (1998).
    [Crossref]
  14. E. Preston, T. Begman, R. Gorenflo, D. Hermann, E. Kopala, T. Kuzma, L. Lazofson, and R. Orkis, “Development of a field-portable imaging system for scene classification using multispectral data fusion algorithms,” IEEE Aerosp. Electron. Syst. Mag. 9, 13-19 (1994).
    [Crossref]
  15. R. Shogenji, Y. Kitamura, K. Yamada, S. Miyatake, and J. Tanida, “Multispectral imaging using compact compound optics,” Opt. Express 12, 1643-1655 (2004).
    [Crossref] [PubMed]
  16. B. Zitová and J. Flusser, “Image registration methods: a survey,” Image Vision Comput. 21, 977-1000 (2003).
    [Crossref]
  17. J. Batlle, J. Marti, P. Ridao, and J. Amat, “A new FPGA/DSP-based parallel architecture for real-time image processing,” Real-Time Imag. 8, 345-356 (2002).
    [Crossref]
  18. J. C. Ramella-Roman and S. A. Mathews, “Spectroscopic measurement of oxygen saturation in the retina,” IEEE J. Sel. Top. Quantum Electron. 13, 1697-1703 (2007).
    [Crossref]
  19. R. Barnard, V. P. Pauca, T. C. Torgersen, R. J. Plemmons, S. Prasad, J. van der Gracht, J. Nagy, J. Chung, G. Behrmann, S. Mathews, and M. Mirotznik, “High-resolution iris image reconstruction from low-resolution imagery,” Proc. SPIE 6313, D1-D13 (2006).

2008 (1)

2007 (2)

M. J. Wabomba, Y. Sulub, and G. W. Small, “Remote detection of volatile organic compounds by passive multispectral infrared imaging measurements,” Appl. Spectrosc. 61, 349-358(2007).
[Crossref] [PubMed]

J. C. Ramella-Roman and S. A. Mathews, “Spectroscopic measurement of oxygen saturation in the retina,” IEEE J. Sel. Top. Quantum Electron. 13, 1697-1703 (2007).
[Crossref]

2006 (2)

R. Barnard, V. P. Pauca, T. C. Torgersen, R. J. Plemmons, S. Prasad, J. van der Gracht, J. Nagy, J. Chung, G. Behrmann, S. Mathews, and M. Mirotznik, “High-resolution iris image reconstruction from low-resolution imagery,” Proc. SPIE 6313, D1-D13 (2006).

H. Ren, Q. Du, J. Wang, C.-I. Chang, J. O. Jensen, and J. L. Jensen, “Automatic target recognition for hyperspectral imagery using high-order statistics,” IEEE Trans. Aerosp. Electron. Syst. 42, 1372-1385 (2006).
[Crossref]

2005 (1)

J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, “Classification of hyperspectral data from urban areas based on extended morphological profiles,” IEEE Trans. Geosci. Remote Sens. 43, 480-491 (2005).
[Crossref]

2004 (2)

T. Vo-Dinh, “A hyperspectral imaging system for in vivo optical diagnostics,” IEEE Eng. Med. Biol. Magazine 23(5), 40-49 (2004).
[Crossref]

R. Shogenji, Y. Kitamura, K. Yamada, S. Miyatake, and J. Tanida, “Multispectral imaging using compact compound optics,” Opt. Express 12, 1643-1655 (2004).
[Crossref] [PubMed]

2003 (1)

B. Zitová and J. Flusser, “Image registration methods: a survey,” Image Vision Comput. 21, 977-1000 (2003).
[Crossref]

2002 (1)

J. Batlle, J. Marti, P. Ridao, and J. Amat, “A new FPGA/DSP-based parallel architecture for real-time image processing,” Real-Time Imag. 8, 345-356 (2002).
[Crossref]

1998 (2)

C.-I. Chang, E. Sun, and M. L. G. Althouse, “An unsupervised interference rejection approach to target detection and classification for hyperspectral imagery,” Opt. Eng. 37, 735-743(1998).
[Crossref]

J. R. Mansfield, M. G. Sowa, J. R. Payette, B. Abdulrauf, M. F. Stranc, and H. H. Mantsch, “Tissue viability by multispectral near infrared imaging: a Fuzzy C-Mean Clustering Analysis,” IEEE Trans. Med. Imaging 17, 1011-1018 (1998).
[Crossref]

1994 (1)

E. Preston, T. Begman, R. Gorenflo, D. Hermann, E. Kopala, T. Kuzma, L. Lazofson, and R. Orkis, “Development of a field-portable imaging system for scene classification using multispectral data fusion algorithms,” IEEE Aerosp. Electron. Syst. Mag. 9, 13-19 (1994).
[Crossref]

Abdulrauf, B.

J. R. Mansfield, M. G. Sowa, J. R. Payette, B. Abdulrauf, M. F. Stranc, and H. H. Mantsch, “Tissue viability by multispectral near infrared imaging: a Fuzzy C-Mean Clustering Analysis,” IEEE Trans. Med. Imaging 17, 1011-1018 (1998).
[Crossref]

Althouse, M. L. G.

C.-I. Chang, E. Sun, and M. L. G. Althouse, “An unsupervised interference rejection approach to target detection and classification for hyperspectral imagery,” Opt. Eng. 37, 735-743(1998).
[Crossref]

Amat, J.

J. Batlle, J. Marti, P. Ridao, and J. Amat, “A new FPGA/DSP-based parallel architecture for real-time image processing,” Real-Time Imag. 8, 345-356 (2002).
[Crossref]

Barnard, R.

R. Barnard, V. P. Pauca, T. C. Torgersen, R. J. Plemmons, S. Prasad, J. van der Gracht, J. Nagy, J. Chung, G. Behrmann, S. Mathews, and M. Mirotznik, “High-resolution iris image reconstruction from low-resolution imagery,” Proc. SPIE 6313, D1-D13 (2006).

Batlle, J.

J. Batlle, J. Marti, P. Ridao, and J. Amat, “A new FPGA/DSP-based parallel architecture for real-time image processing,” Real-Time Imag. 8, 345-356 (2002).
[Crossref]

Begman, T.

E. Preston, T. Begman, R. Gorenflo, D. Hermann, E. Kopala, T. Kuzma, L. Lazofson, and R. Orkis, “Development of a field-portable imaging system for scene classification using multispectral data fusion algorithms,” IEEE Aerosp. Electron. Syst. Mag. 9, 13-19 (1994).
[Crossref]

Behrmann, G.

R. Barnard, V. P. Pauca, T. C. Torgersen, R. J. Plemmons, S. Prasad, J. van der Gracht, J. Nagy, J. Chung, G. Behrmann, S. Mathews, and M. Mirotznik, “High-resolution iris image reconstruction from low-resolution imagery,” Proc. SPIE 6313, D1-D13 (2006).

Benediktsson, J. A.

J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, “Classification of hyperspectral data from urban areas based on extended morphological profiles,” IEEE Trans. Geosci. Remote Sens. 43, 480-491 (2005).
[Crossref]

Borowski, B.

A. Weisberg, M. Najarian, B. Borowski, J. Lisowski, and B. Miller, “Spectral angle automatic cluster routine (SAALT): an unsupervised multispectral clustering algorithm,” in IEEE Proceedings, Aerospace Conference 1999 (IEEE, 1999), pp. 307-317.

Boyce, C.

C. Boyce, A. Ross, M. Monaco, L. Hornak, and X. Li, “Multispectral iris analysis: a preliminary study,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2006), pp. 51-51.

Brozk, S. M.

J. A. Timlin, M. B. Sinclair, D. M. Haaland, J. Martinez, M. Manginell, S. M. Brozk, J. F. Guzowski, and M. Werner-Washburne, “Hyperspectral imaging of biological targets: the difference a high resolution spectral dimension and multivariate analysis can make,” in IEEE Symposium on Biomedical Imaging (IEEE, 2004), Vol. 2, pp. 1529-1532.

Chang, C.-I.

H. Ren, Q. Du, J. Wang, C.-I. Chang, J. O. Jensen, and J. L. Jensen, “Automatic target recognition for hyperspectral imagery using high-order statistics,” IEEE Trans. Aerosp. Electron. Syst. 42, 1372-1385 (2006).
[Crossref]

C.-I. Chang, E. Sun, and M. L. G. Althouse, “An unsupervised interference rejection approach to target detection and classification for hyperspectral imagery,” Opt. Eng. 37, 735-743(1998).
[Crossref]

Chen, H.

D. G. Goodenough, J. Pearlman, H. Chen, A. Dyk, T. Han, J. Li, J. Miller, and K. O. Niemann, “Forest information from hyperspectral sensing,” in IEEE Proceedings on Geoscience and Remote Sensing (IEEE, 2004), Vol. 4, pp. 2585-2589.

Chung, J.

R. Barnard, V. P. Pauca, T. C. Torgersen, R. J. Plemmons, S. Prasad, J. van der Gracht, J. Nagy, J. Chung, G. Behrmann, S. Mathews, and M. Mirotznik, “High-resolution iris image reconstruction from low-resolution imagery,” Proc. SPIE 6313, D1-D13 (2006).

Du, Q.

H. Ren, Q. Du, J. Wang, C.-I. Chang, J. O. Jensen, and J. L. Jensen, “Automatic target recognition for hyperspectral imagery using high-order statistics,” IEEE Trans. Aerosp. Electron. Syst. 42, 1372-1385 (2006).
[Crossref]

Dyk, A.

D. G. Goodenough, J. Pearlman, H. Chen, A. Dyk, T. Han, J. Li, J. Miller, and K. O. Niemann, “Forest information from hyperspectral sensing,” in IEEE Proceedings on Geoscience and Remote Sensing (IEEE, 2004), Vol. 4, pp. 2585-2589.

Flusser, J.

B. Zitová and J. Flusser, “Image registration methods: a survey,” Image Vision Comput. 21, 977-1000 (2003).
[Crossref]

Goodenough, D. G.

D. G. Goodenough, J. Pearlman, H. Chen, A. Dyk, T. Han, J. Li, J. Miller, and K. O. Niemann, “Forest information from hyperspectral sensing,” in IEEE Proceedings on Geoscience and Remote Sensing (IEEE, 2004), Vol. 4, pp. 2585-2589.

Gorenflo, R.

E. Preston, T. Begman, R. Gorenflo, D. Hermann, E. Kopala, T. Kuzma, L. Lazofson, and R. Orkis, “Development of a field-portable imaging system for scene classification using multispectral data fusion algorithms,” IEEE Aerosp. Electron. Syst. Mag. 9, 13-19 (1994).
[Crossref]

Guzowski, J. F.

J. A. Timlin, M. B. Sinclair, D. M. Haaland, J. Martinez, M. Manginell, S. M. Brozk, J. F. Guzowski, and M. Werner-Washburne, “Hyperspectral imaging of biological targets: the difference a high resolution spectral dimension and multivariate analysis can make,” in IEEE Symposium on Biomedical Imaging (IEEE, 2004), Vol. 2, pp. 1529-1532.

Haaland, D. M.

J. A. Timlin, M. B. Sinclair, D. M. Haaland, J. Martinez, M. Manginell, S. M. Brozk, J. F. Guzowski, and M. Werner-Washburne, “Hyperspectral imaging of biological targets: the difference a high resolution spectral dimension and multivariate analysis can make,” in IEEE Symposium on Biomedical Imaging (IEEE, 2004), Vol. 2, pp. 1529-1532.

Han, T.

D. G. Goodenough, J. Pearlman, H. Chen, A. Dyk, T. Han, J. Li, J. Miller, and K. O. Niemann, “Forest information from hyperspectral sensing,” in IEEE Proceedings on Geoscience and Remote Sensing (IEEE, 2004), Vol. 4, pp. 2585-2589.

Hecht, E.

E. Hecht, “Familiar aspects of the interaction of light and matter,” in Optics, 4th ed. (Addison-Wesley, 2002), pp. 131-136.

Hermann, D.

E. Preston, T. Begman, R. Gorenflo, D. Hermann, E. Kopala, T. Kuzma, L. Lazofson, and R. Orkis, “Development of a field-portable imaging system for scene classification using multispectral data fusion algorithms,” IEEE Aerosp. Electron. Syst. Mag. 9, 13-19 (1994).
[Crossref]

Hornak, L.

C. Boyce, A. Ross, M. Monaco, L. Hornak, and X. Li, “Multispectral iris analysis: a preliminary study,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2006), pp. 51-51.

Jensen, J. L.

H. Ren, Q. Du, J. Wang, C.-I. Chang, J. O. Jensen, and J. L. Jensen, “Automatic target recognition for hyperspectral imagery using high-order statistics,” IEEE Trans. Aerosp. Electron. Syst. 42, 1372-1385 (2006).
[Crossref]

Jensen, J. O.

H. Ren, Q. Du, J. Wang, C.-I. Chang, J. O. Jensen, and J. L. Jensen, “Automatic target recognition for hyperspectral imagery using high-order statistics,” IEEE Trans. Aerosp. Electron. Syst. 42, 1372-1385 (2006).
[Crossref]

Kitamura, Y.

Kopala, E.

E. Preston, T. Begman, R. Gorenflo, D. Hermann, E. Kopala, T. Kuzma, L. Lazofson, and R. Orkis, “Development of a field-portable imaging system for scene classification using multispectral data fusion algorithms,” IEEE Aerosp. Electron. Syst. Mag. 9, 13-19 (1994).
[Crossref]

Kuzma, T.

E. Preston, T. Begman, R. Gorenflo, D. Hermann, E. Kopala, T. Kuzma, L. Lazofson, and R. Orkis, “Development of a field-portable imaging system for scene classification using multispectral data fusion algorithms,” IEEE Aerosp. Electron. Syst. Mag. 9, 13-19 (1994).
[Crossref]

Lazofson, L.

E. Preston, T. Begman, R. Gorenflo, D. Hermann, E. Kopala, T. Kuzma, L. Lazofson, and R. Orkis, “Development of a field-portable imaging system for scene classification using multispectral data fusion algorithms,” IEEE Aerosp. Electron. Syst. Mag. 9, 13-19 (1994).
[Crossref]

Li, J.

D. G. Goodenough, J. Pearlman, H. Chen, A. Dyk, T. Han, J. Li, J. Miller, and K. O. Niemann, “Forest information from hyperspectral sensing,” in IEEE Proceedings on Geoscience and Remote Sensing (IEEE, 2004), Vol. 4, pp. 2585-2589.

Li, X.

C. Boyce, A. Ross, M. Monaco, L. Hornak, and X. Li, “Multispectral iris analysis: a preliminary study,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2006), pp. 51-51.

Lisowski, J.

A. Weisberg, M. Najarian, B. Borowski, J. Lisowski, and B. Miller, “Spectral angle automatic cluster routine (SAALT): an unsupervised multispectral clustering algorithm,” in IEEE Proceedings, Aerospace Conference 1999 (IEEE, 1999), pp. 307-317.

Manginell, M.

J. A. Timlin, M. B. Sinclair, D. M. Haaland, J. Martinez, M. Manginell, S. M. Brozk, J. F. Guzowski, and M. Werner-Washburne, “Hyperspectral imaging of biological targets: the difference a high resolution spectral dimension and multivariate analysis can make,” in IEEE Symposium on Biomedical Imaging (IEEE, 2004), Vol. 2, pp. 1529-1532.

Mansfield, J. R.

J. R. Mansfield, M. G. Sowa, J. R. Payette, B. Abdulrauf, M. F. Stranc, and H. H. Mantsch, “Tissue viability by multispectral near infrared imaging: a Fuzzy C-Mean Clustering Analysis,” IEEE Trans. Med. Imaging 17, 1011-1018 (1998).
[Crossref]

Mantsch, H. H.

J. R. Mansfield, M. G. Sowa, J. R. Payette, B. Abdulrauf, M. F. Stranc, and H. H. Mantsch, “Tissue viability by multispectral near infrared imaging: a Fuzzy C-Mean Clustering Analysis,” IEEE Trans. Med. Imaging 17, 1011-1018 (1998).
[Crossref]

Marti, J.

J. Batlle, J. Marti, P. Ridao, and J. Amat, “A new FPGA/DSP-based parallel architecture for real-time image processing,” Real-Time Imag. 8, 345-356 (2002).
[Crossref]

Martinez, J.

J. A. Timlin, M. B. Sinclair, D. M. Haaland, J. Martinez, M. Manginell, S. M. Brozk, J. F. Guzowski, and M. Werner-Washburne, “Hyperspectral imaging of biological targets: the difference a high resolution spectral dimension and multivariate analysis can make,” in IEEE Symposium on Biomedical Imaging (IEEE, 2004), Vol. 2, pp. 1529-1532.

Mathews, S.

R. Barnard, V. P. Pauca, T. C. Torgersen, R. J. Plemmons, S. Prasad, J. van der Gracht, J. Nagy, J. Chung, G. Behrmann, S. Mathews, and M. Mirotznik, “High-resolution iris image reconstruction from low-resolution imagery,” Proc. SPIE 6313, D1-D13 (2006).

Mathews, S. A.

J. C. Ramella-Roman and S. A. Mathews, “Spectroscopic measurement of oxygen saturation in the retina,” IEEE J. Sel. Top. Quantum Electron. 13, 1697-1703 (2007).
[Crossref]

Miller, B.

A. Weisberg, M. Najarian, B. Borowski, J. Lisowski, and B. Miller, “Spectral angle automatic cluster routine (SAALT): an unsupervised multispectral clustering algorithm,” in IEEE Proceedings, Aerospace Conference 1999 (IEEE, 1999), pp. 307-317.

Miller, J.

D. G. Goodenough, J. Pearlman, H. Chen, A. Dyk, T. Han, J. Li, J. Miller, and K. O. Niemann, “Forest information from hyperspectral sensing,” in IEEE Proceedings on Geoscience and Remote Sensing (IEEE, 2004), Vol. 4, pp. 2585-2589.

Mirotznik, M.

R. Barnard, V. P. Pauca, T. C. Torgersen, R. J. Plemmons, S. Prasad, J. van der Gracht, J. Nagy, J. Chung, G. Behrmann, S. Mathews, and M. Mirotznik, “High-resolution iris image reconstruction from low-resolution imagery,” Proc. SPIE 6313, D1-D13 (2006).

Miyatake, S.

Monaco, M.

C. Boyce, A. Ross, M. Monaco, L. Hornak, and X. Li, “Multispectral iris analysis: a preliminary study,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2006), pp. 51-51.

Nagy, J.

R. Barnard, V. P. Pauca, T. C. Torgersen, R. J. Plemmons, S. Prasad, J. van der Gracht, J. Nagy, J. Chung, G. Behrmann, S. Mathews, and M. Mirotznik, “High-resolution iris image reconstruction from low-resolution imagery,” Proc. SPIE 6313, D1-D13 (2006).

Najarian, M.

A. Weisberg, M. Najarian, B. Borowski, J. Lisowski, and B. Miller, “Spectral angle automatic cluster routine (SAALT): an unsupervised multispectral clustering algorithm,” in IEEE Proceedings, Aerospace Conference 1999 (IEEE, 1999), pp. 307-317.

Niemann, K. O.

D. G. Goodenough, J. Pearlman, H. Chen, A. Dyk, T. Han, J. Li, J. Miller, and K. O. Niemann, “Forest information from hyperspectral sensing,” in IEEE Proceedings on Geoscience and Remote Sensing (IEEE, 2004), Vol. 4, pp. 2585-2589.

Ntziachristos, V.

Orkis, R.

E. Preston, T. Begman, R. Gorenflo, D. Hermann, E. Kopala, T. Kuzma, L. Lazofson, and R. Orkis, “Development of a field-portable imaging system for scene classification using multispectral data fusion algorithms,” IEEE Aerosp. Electron. Syst. Mag. 9, 13-19 (1994).
[Crossref]

Palmason, J. A.

J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, “Classification of hyperspectral data from urban areas based on extended morphological profiles,” IEEE Trans. Geosci. Remote Sens. 43, 480-491 (2005).
[Crossref]

Pauca, V. P.

R. Barnard, V. P. Pauca, T. C. Torgersen, R. J. Plemmons, S. Prasad, J. van der Gracht, J. Nagy, J. Chung, G. Behrmann, S. Mathews, and M. Mirotznik, “High-resolution iris image reconstruction from low-resolution imagery,” Proc. SPIE 6313, D1-D13 (2006).

Payette, J. R.

J. R. Mansfield, M. G. Sowa, J. R. Payette, B. Abdulrauf, M. F. Stranc, and H. H. Mantsch, “Tissue viability by multispectral near infrared imaging: a Fuzzy C-Mean Clustering Analysis,” IEEE Trans. Med. Imaging 17, 1011-1018 (1998).
[Crossref]

Pearlman, J.

D. G. Goodenough, J. Pearlman, H. Chen, A. Dyk, T. Han, J. Li, J. Miller, and K. O. Niemann, “Forest information from hyperspectral sensing,” in IEEE Proceedings on Geoscience and Remote Sensing (IEEE, 2004), Vol. 4, pp. 2585-2589.

Plemmons, R. J.

R. Barnard, V. P. Pauca, T. C. Torgersen, R. J. Plemmons, S. Prasad, J. van der Gracht, J. Nagy, J. Chung, G. Behrmann, S. Mathews, and M. Mirotznik, “High-resolution iris image reconstruction from low-resolution imagery,” Proc. SPIE 6313, D1-D13 (2006).

Prasad, S.

R. Barnard, V. P. Pauca, T. C. Torgersen, R. J. Plemmons, S. Prasad, J. van der Gracht, J. Nagy, J. Chung, G. Behrmann, S. Mathews, and M. Mirotznik, “High-resolution iris image reconstruction from low-resolution imagery,” Proc. SPIE 6313, D1-D13 (2006).

Preston, E.

E. Preston, T. Begman, R. Gorenflo, D. Hermann, E. Kopala, T. Kuzma, L. Lazofson, and R. Orkis, “Development of a field-portable imaging system for scene classification using multispectral data fusion algorithms,” IEEE Aerosp. Electron. Syst. Mag. 9, 13-19 (1994).
[Crossref]

Ramella-Roman, J. C.

J. C. Ramella-Roman and S. A. Mathews, “Spectroscopic measurement of oxygen saturation in the retina,” IEEE J. Sel. Top. Quantum Electron. 13, 1697-1703 (2007).
[Crossref]

Ren, H.

H. Ren, Q. Du, J. Wang, C.-I. Chang, J. O. Jensen, and J. L. Jensen, “Automatic target recognition for hyperspectral imagery using high-order statistics,” IEEE Trans. Aerosp. Electron. Syst. 42, 1372-1385 (2006).
[Crossref]

Ridao, P.

J. Batlle, J. Marti, P. Ridao, and J. Amat, “A new FPGA/DSP-based parallel architecture for real-time image processing,” Real-Time Imag. 8, 345-356 (2002).
[Crossref]

Ross, A.

C. Boyce, A. Ross, M. Monaco, L. Hornak, and X. Li, “Multispectral iris analysis: a preliminary study,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2006), pp. 51-51.

Shogenji, R.

Sinclair, M. B.

J. A. Timlin, M. B. Sinclair, D. M. Haaland, J. Martinez, M. Manginell, S. M. Brozk, J. F. Guzowski, and M. Werner-Washburne, “Hyperspectral imaging of biological targets: the difference a high resolution spectral dimension and multivariate analysis can make,” in IEEE Symposium on Biomedical Imaging (IEEE, 2004), Vol. 2, pp. 1529-1532.

Small, G. W.

Sowa, M. G.

J. R. Mansfield, M. G. Sowa, J. R. Payette, B. Abdulrauf, M. F. Stranc, and H. H. Mantsch, “Tissue viability by multispectral near infrared imaging: a Fuzzy C-Mean Clustering Analysis,” IEEE Trans. Med. Imaging 17, 1011-1018 (1998).
[Crossref]

Stranc, M. F.

J. R. Mansfield, M. G. Sowa, J. R. Payette, B. Abdulrauf, M. F. Stranc, and H. H. Mantsch, “Tissue viability by multispectral near infrared imaging: a Fuzzy C-Mean Clustering Analysis,” IEEE Trans. Med. Imaging 17, 1011-1018 (1998).
[Crossref]

Sulub, Y.

Sun, E.

C.-I. Chang, E. Sun, and M. L. G. Althouse, “An unsupervised interference rejection approach to target detection and classification for hyperspectral imagery,” Opt. Eng. 37, 735-743(1998).
[Crossref]

Sveinsson, J. R.

J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, “Classification of hyperspectral data from urban areas based on extended morphological profiles,” IEEE Trans. Geosci. Remote Sens. 43, 480-491 (2005).
[Crossref]

Tanida, J.

Themelis, G.

Timlin, J. A.

J. A. Timlin, M. B. Sinclair, D. M. Haaland, J. Martinez, M. Manginell, S. M. Brozk, J. F. Guzowski, and M. Werner-Washburne, “Hyperspectral imaging of biological targets: the difference a high resolution spectral dimension and multivariate analysis can make,” in IEEE Symposium on Biomedical Imaging (IEEE, 2004), Vol. 2, pp. 1529-1532.

Torgersen, T. C.

R. Barnard, V. P. Pauca, T. C. Torgersen, R. J. Plemmons, S. Prasad, J. van der Gracht, J. Nagy, J. Chung, G. Behrmann, S. Mathews, and M. Mirotznik, “High-resolution iris image reconstruction from low-resolution imagery,” Proc. SPIE 6313, D1-D13 (2006).

van der Gracht, J.

R. Barnard, V. P. Pauca, T. C. Torgersen, R. J. Plemmons, S. Prasad, J. van der Gracht, J. Nagy, J. Chung, G. Behrmann, S. Mathews, and M. Mirotznik, “High-resolution iris image reconstruction from low-resolution imagery,” Proc. SPIE 6313, D1-D13 (2006).

Vo-Dinh, T.

T. Vo-Dinh, “A hyperspectral imaging system for in vivo optical diagnostics,” IEEE Eng. Med. Biol. Magazine 23(5), 40-49 (2004).
[Crossref]

Wabomba, M. J.

Wang, J.

H. Ren, Q. Du, J. Wang, C.-I. Chang, J. O. Jensen, and J. L. Jensen, “Automatic target recognition for hyperspectral imagery using high-order statistics,” IEEE Trans. Aerosp. Electron. Syst. 42, 1372-1385 (2006).
[Crossref]

Weisberg, A.

A. Weisberg, M. Najarian, B. Borowski, J. Lisowski, and B. Miller, “Spectral angle automatic cluster routine (SAALT): an unsupervised multispectral clustering algorithm,” in IEEE Proceedings, Aerospace Conference 1999 (IEEE, 1999), pp. 307-317.

Werner-Washburne, M.

J. A. Timlin, M. B. Sinclair, D. M. Haaland, J. Martinez, M. Manginell, S. M. Brozk, J. F. Guzowski, and M. Werner-Washburne, “Hyperspectral imaging of biological targets: the difference a high resolution spectral dimension and multivariate analysis can make,” in IEEE Symposium on Biomedical Imaging (IEEE, 2004), Vol. 2, pp. 1529-1532.

Winter, E. M.

E. M. Winter, “Methods for determining best multispectral bands using hyperspectral data,” in IEEE Aerospace Conference (IEEE, 2007), pp. 1-6.
[Crossref]

Yamada, K.

Yoo, J. S.

Zitová, B.

B. Zitová and J. Flusser, “Image registration methods: a survey,” Image Vision Comput. 21, 977-1000 (2003).
[Crossref]

Appl. Spectrosc. (1)

IEEE Aerosp. Electron. Syst. Mag. (1)

E. Preston, T. Begman, R. Gorenflo, D. Hermann, E. Kopala, T. Kuzma, L. Lazofson, and R. Orkis, “Development of a field-portable imaging system for scene classification using multispectral data fusion algorithms,” IEEE Aerosp. Electron. Syst. Mag. 9, 13-19 (1994).
[Crossref]

IEEE Eng. Med. Biol. Magazine (1)

T. Vo-Dinh, “A hyperspectral imaging system for in vivo optical diagnostics,” IEEE Eng. Med. Biol. Magazine 23(5), 40-49 (2004).
[Crossref]

IEEE J. Sel. Top. Quantum Electron. (1)

J. C. Ramella-Roman and S. A. Mathews, “Spectroscopic measurement of oxygen saturation in the retina,” IEEE J. Sel. Top. Quantum Electron. 13, 1697-1703 (2007).
[Crossref]

IEEE Trans. Aerosp. Electron. Syst. (1)

H. Ren, Q. Du, J. Wang, C.-I. Chang, J. O. Jensen, and J. L. Jensen, “Automatic target recognition for hyperspectral imagery using high-order statistics,” IEEE Trans. Aerosp. Electron. Syst. 42, 1372-1385 (2006).
[Crossref]

IEEE Trans. Geosci. Remote Sens. (1)

J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, “Classification of hyperspectral data from urban areas based on extended morphological profiles,” IEEE Trans. Geosci. Remote Sens. 43, 480-491 (2005).
[Crossref]

IEEE Trans. Med. Imaging (1)

J. R. Mansfield, M. G. Sowa, J. R. Payette, B. Abdulrauf, M. F. Stranc, and H. H. Mantsch, “Tissue viability by multispectral near infrared imaging: a Fuzzy C-Mean Clustering Analysis,” IEEE Trans. Med. Imaging 17, 1011-1018 (1998).
[Crossref]

Image Vision Comput. (1)

B. Zitová and J. Flusser, “Image registration methods: a survey,” Image Vision Comput. 21, 977-1000 (2003).
[Crossref]

Opt. Eng. (1)

C.-I. Chang, E. Sun, and M. L. G. Althouse, “An unsupervised interference rejection approach to target detection and classification for hyperspectral imagery,” Opt. Eng. 37, 735-743(1998).
[Crossref]

Opt. Express (1)

Opt. Lett. (1)

Proc. SPIE (1)

R. Barnard, V. P. Pauca, T. C. Torgersen, R. J. Plemmons, S. Prasad, J. van der Gracht, J. Nagy, J. Chung, G. Behrmann, S. Mathews, and M. Mirotznik, “High-resolution iris image reconstruction from low-resolution imagery,” Proc. SPIE 6313, D1-D13 (2006).

Real-Time Imag. (1)

J. Batlle, J. Marti, P. Ridao, and J. Amat, “A new FPGA/DSP-based parallel architecture for real-time image processing,” Real-Time Imag. 8, 345-356 (2002).
[Crossref]

Other (6)

A. Weisberg, M. Najarian, B. Borowski, J. Lisowski, and B. Miller, “Spectral angle automatic cluster routine (SAALT): an unsupervised multispectral clustering algorithm,” in IEEE Proceedings, Aerospace Conference 1999 (IEEE, 1999), pp. 307-317.

E. M. Winter, “Methods for determining best multispectral bands using hyperspectral data,” in IEEE Aerospace Conference (IEEE, 2007), pp. 1-6.
[Crossref]

E. Hecht, “Familiar aspects of the interaction of light and matter,” in Optics, 4th ed. (Addison-Wesley, 2002), pp. 131-136.

C. Boyce, A. Ross, M. Monaco, L. Hornak, and X. Li, “Multispectral iris analysis: a preliminary study,” in IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2006), pp. 51-51.

D. G. Goodenough, J. Pearlman, H. Chen, A. Dyk, T. Han, J. Li, J. Miller, and K. O. Niemann, “Forest information from hyperspectral sensing,” in IEEE Proceedings on Geoscience and Remote Sensing (IEEE, 2004), Vol. 4, pp. 2585-2589.

J. A. Timlin, M. B. Sinclair, D. M. Haaland, J. Martinez, M. Manginell, S. M. Brozk, J. F. Guzowski, and M. Werner-Washburne, “Hyperspectral imaging of biological targets: the difference a high resolution spectral dimension and multivariate analysis can make,” in IEEE Symposium on Biomedical Imaging (IEEE, 2004), Vol. 2, pp. 1529-1532.

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Figures (7)

Fig. 1
Fig. 1

Photograph of a lens plate containing 18 lens assemblies in a hexagonally close-packed array.

Fig. 2
Fig. 2

The complete multispectral camera, including the objective assembly and integrated ring illuminator.

Fig. 3
Fig. 3

(a) Complete image of the National Shrine of the Immaculate Conception acquired with the multispectral camera. (b) The Shrine photographed with a conventional digital camera.

Fig. 4
Fig. 4

(a) Macbeth color chart photographed with a conventional camera. (b) Composite image showing red enhancement. (c) Composite image showing blue enhancement.

Fig. 5
Fig. 5

Diffuse reflectance as a function of wavelength for blue and yellow Spectralon color standards measured using a spectrophotometer and multispectral camera.

Fig. 6
Fig. 6

Diffuse reflectance as a function of wavelength for green and red Spectralon color standards measured using a spectrophotometer and multispectral camera.

Fig. 7
Fig. 7

Portion of a single subimage, showing a 1951 U.S. Air Force resolution target. The image indicates a resolution greater than 2     line pairs / mm .

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