P. Koirala, M. Hauta-Kasari, and J. Parkkinen, “Highlight removal from single image,” in Proceedings of Advanced Concepts for Intelligent Vision Systems (Springer, 2009), Vol. 5807, pp. 176–187.

P. Stigel, K. Miyata, and M. Hauta-Kasari, “Wiener estimation method in estimating of spectral reflectance from RGB images,” Pattern Recogn. Image Anal. 17, 233–242 (2007).

[CrossRef]

Z. Fu, R. T. Tan, and T. Caelli, “Specular free spectral imaging using orthogonal subspace projection,” in Proceedings of International Conference on Pattern Recognition (IEEE Computer Society, 2006), pp. 812–815.

V. Bochko and J. Parkkinen, “Highlight analysis using a mixture model of probabilistic PCA,” in Proceedings of the 4th WSEAS International Conference on Signal Processing, Robotics and Automation (World Scientific and Engineering Academy and Society, 2005), paper 15.

C.-I. Chang, “Orthogonal subspace projection (OSP) revisited: a comprehensive study and analysis,” IEEE Trans. Geosci. Remote Sens. 43, 502–518 (2005).

[CrossRef]

Q. Du, I. Kopriva, and H. Szu, “Investigation on constrained matrix factorization for hyperspectral image analysis,” in IEEE International Geoscience and Remote Sensing Symposium Proceedings (IEEE International, 2005), pp. 4304–4306.

R. T. Tan and K. Ikeuchi, “Seperating reflection components of textured surfaces using a single image,” IEEE Trans. Pattern Anal. Machine Intell. 27, 178–193 (2005).

[CrossRef]

C.-I. Chang and Q. Du, “Estimation of number of spectrally distinct signal sources in hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 42, 608–619 (2004).

[CrossRef]

P. Tan, S. Lin, L. Quan, and H-Y. Shum, “Highlight removal by illumination-constrained inpainting,” in Proceedings of the 9th IEEE International Conference on Computer Vision (IEEE Computer Society, 2003), pp. 164–169.

N. Keshava, “A survey of spectral unmixing algorithms,” Lincoln Lab. J. 14, 55–78 (2003).

H. Ren and C-I. Chang, “Automatic spectral target recognition in hyper spectral imagery,” IEEE Trans. Aerosp. Electron. Syst. 39, 1232–1248 (2003).

[CrossRef]

S. K. Nayar, X. S. Fang, and T. Boult, “Separation of reflection components using color and polarization,” Int. J. Comput. Vision 21, 163–186 (1997).

[CrossRef]

J. C. Harsanyi, “Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach,” IEEE Trans. Geosci. Remote Sens. 32, 779–785(1994).

[CrossRef]

R. H. Yuhas, A. F. H. Goetz, and J. W. Boardman, “Discrimination among semiarid landscape endmembers using the spectral angle mapper (SAM) algorithm,” in Summaries of the Third Annual JPL Airborne Geoscience Workshop, Publication 92-14 (Jet Propulsion Laboratory, 1992), Vol. 1, pp. 147–149.

S. A. Shafer, “Using color to separate reflection components,” Color Res. Appl. 10, 210–218 (1985).

[CrossRef]

O. L. Frost III, “An algorithm for linearly constrained adaptive array processing,” Proc. IEEE 60, 926–935 (1972).

[CrossRef]

R. H. Yuhas, A. F. H. Goetz, and J. W. Boardman, “Discrimination among semiarid landscape endmembers using the spectral angle mapper (SAM) algorithm,” in Summaries of the Third Annual JPL Airborne Geoscience Workshop, Publication 92-14 (Jet Propulsion Laboratory, 1992), Vol. 1, pp. 147–149.

V. Bochko and J. Parkkinen, “Highlight analysis using a mixture model of probabilistic PCA,” in Proceedings of the 4th WSEAS International Conference on Signal Processing, Robotics and Automation (World Scientific and Engineering Academy and Society, 2005), paper 15.

S. K. Nayar, X. S. Fang, and T. Boult, “Separation of reflection components using color and polarization,” Int. J. Comput. Vision 21, 163–186 (1997).

[CrossRef]

Z. Fu, R. T. Tan, and T. Caelli, “Specular free spectral imaging using orthogonal subspace projection,” in Proceedings of International Conference on Pattern Recognition (IEEE Computer Society, 2006), pp. 812–815.

C.-I. Chang, “Orthogonal subspace projection (OSP) revisited: a comprehensive study and analysis,” IEEE Trans. Geosci. Remote Sens. 43, 502–518 (2005).

[CrossRef]

C.-I. Chang and Q. Du, “Estimation of number of spectrally distinct signal sources in hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 42, 608–619 (2004).

[CrossRef]

H. Ren and C-I. Chang, “Automatic spectral target recognition in hyper spectral imagery,” IEEE Trans. Aerosp. Electron. Syst. 39, 1232–1248 (2003).

[CrossRef]

Q. Du, I. Kopriva, and H. Szu, “Investigation on constrained matrix factorization for hyperspectral image analysis,” in IEEE International Geoscience and Remote Sensing Symposium Proceedings (IEEE International, 2005), pp. 4304–4306.

C.-I. Chang and Q. Du, “Estimation of number of spectrally distinct signal sources in hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 42, 608–619 (2004).

[CrossRef]

S. K. Nayar, X. S. Fang, and T. Boult, “Separation of reflection components using color and polarization,” Int. J. Comput. Vision 21, 163–186 (1997).

[CrossRef]

O. L. Frost III, “An algorithm for linearly constrained adaptive array processing,” Proc. IEEE 60, 926–935 (1972).

[CrossRef]

Z. Fu, R. T. Tan, and T. Caelli, “Specular free spectral imaging using orthogonal subspace projection,” in Proceedings of International Conference on Pattern Recognition (IEEE Computer Society, 2006), pp. 812–815.

R. H. Yuhas, A. F. H. Goetz, and J. W. Boardman, “Discrimination among semiarid landscape endmembers using the spectral angle mapper (SAM) algorithm,” in Summaries of the Third Annual JPL Airborne Geoscience Workshop, Publication 92-14 (Jet Propulsion Laboratory, 1992), Vol. 1, pp. 147–149.

J. C. Harsanyi, “Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach,” IEEE Trans. Geosci. Remote Sens. 32, 779–785(1994).

[CrossRef]

P. Koirala, M. Hauta-Kasari, and J. Parkkinen, “Highlight removal from single image,” in Proceedings of Advanced Concepts for Intelligent Vision Systems (Springer, 2009), Vol. 5807, pp. 176–187.

P. Stigel, K. Miyata, and M. Hauta-Kasari, “Wiener estimation method in estimating of spectral reflectance from RGB images,” Pattern Recogn. Image Anal. 17, 233–242 (2007).

[CrossRef]

R. T. Tan and K. Ikeuchi, “Seperating reflection components of textured surfaces using a single image,” IEEE Trans. Pattern Anal. Machine Intell. 27, 178–193 (2005).

[CrossRef]

N. Keshava, “A survey of spectral unmixing algorithms,” Lincoln Lab. J. 14, 55–78 (2003).

P. Koirala, M. Hauta-Kasari, and J. Parkkinen, “Highlight removal from single image,” in Proceedings of Advanced Concepts for Intelligent Vision Systems (Springer, 2009), Vol. 5807, pp. 176–187.

Q. Du, I. Kopriva, and H. Szu, “Investigation on constrained matrix factorization for hyperspectral image analysis,” in IEEE International Geoscience and Remote Sensing Symposium Proceedings (IEEE International, 2005), pp. 4304–4306.

P. Tan, S. Lin, L. Quan, and H-Y. Shum, “Highlight removal by illumination-constrained inpainting,” in Proceedings of the 9th IEEE International Conference on Computer Vision (IEEE Computer Society, 2003), pp. 164–169.

P. Stigel, K. Miyata, and M. Hauta-Kasari, “Wiener estimation method in estimating of spectral reflectance from RGB images,” Pattern Recogn. Image Anal. 17, 233–242 (2007).

[CrossRef]

S. K. Nayar, X. S. Fang, and T. Boult, “Separation of reflection components using color and polarization,” Int. J. Comput. Vision 21, 163–186 (1997).

[CrossRef]

P. Koirala, M. Hauta-Kasari, and J. Parkkinen, “Highlight removal from single image,” in Proceedings of Advanced Concepts for Intelligent Vision Systems (Springer, 2009), Vol. 5807, pp. 176–187.

V. Bochko and J. Parkkinen, “Highlight analysis using a mixture model of probabilistic PCA,” in Proceedings of the 4th WSEAS International Conference on Signal Processing, Robotics and Automation (World Scientific and Engineering Academy and Society, 2005), paper 15.

P. Tan, S. Lin, L. Quan, and H-Y. Shum, “Highlight removal by illumination-constrained inpainting,” in Proceedings of the 9th IEEE International Conference on Computer Vision (IEEE Computer Society, 2003), pp. 164–169.

H. Ren and C-I. Chang, “Automatic spectral target recognition in hyper spectral imagery,” IEEE Trans. Aerosp. Electron. Syst. 39, 1232–1248 (2003).

[CrossRef]

S. A. Shafer, “Using color to separate reflection components,” Color Res. Appl. 10, 210–218 (1985).

[CrossRef]

P. Tan, S. Lin, L. Quan, and H-Y. Shum, “Highlight removal by illumination-constrained inpainting,” in Proceedings of the 9th IEEE International Conference on Computer Vision (IEEE Computer Society, 2003), pp. 164–169.

P. Stigel, K. Miyata, and M. Hauta-Kasari, “Wiener estimation method in estimating of spectral reflectance from RGB images,” Pattern Recogn. Image Anal. 17, 233–242 (2007).

[CrossRef]

Q. Du, I. Kopriva, and H. Szu, “Investigation on constrained matrix factorization for hyperspectral image analysis,” in IEEE International Geoscience and Remote Sensing Symposium Proceedings (IEEE International, 2005), pp. 4304–4306.

P. Tan, S. Lin, L. Quan, and H-Y. Shum, “Highlight removal by illumination-constrained inpainting,” in Proceedings of the 9th IEEE International Conference on Computer Vision (IEEE Computer Society, 2003), pp. 164–169.

Z. Fu, R. T. Tan, and T. Caelli, “Specular free spectral imaging using orthogonal subspace projection,” in Proceedings of International Conference on Pattern Recognition (IEEE Computer Society, 2006), pp. 812–815.

R. T. Tan and K. Ikeuchi, “Seperating reflection components of textured surfaces using a single image,” IEEE Trans. Pattern Anal. Machine Intell. 27, 178–193 (2005).

[CrossRef]

R. H. Yuhas, A. F. H. Goetz, and J. W. Boardman, “Discrimination among semiarid landscape endmembers using the spectral angle mapper (SAM) algorithm,” in Summaries of the Third Annual JPL Airborne Geoscience Workshop, Publication 92-14 (Jet Propulsion Laboratory, 1992), Vol. 1, pp. 147–149.

S. A. Shafer, “Using color to separate reflection components,” Color Res. Appl. 10, 210–218 (1985).

[CrossRef]

H. Ren and C-I. Chang, “Automatic spectral target recognition in hyper spectral imagery,” IEEE Trans. Aerosp. Electron. Syst. 39, 1232–1248 (2003).

[CrossRef]

C.-I. Chang and Q. Du, “Estimation of number of spectrally distinct signal sources in hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 42, 608–619 (2004).

[CrossRef]

C.-I. Chang, “Orthogonal subspace projection (OSP) revisited: a comprehensive study and analysis,” IEEE Trans. Geosci. Remote Sens. 43, 502–518 (2005).

[CrossRef]

J. C. Harsanyi, “Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach,” IEEE Trans. Geosci. Remote Sens. 32, 779–785(1994).

[CrossRef]

R. T. Tan and K. Ikeuchi, “Seperating reflection components of textured surfaces using a single image,” IEEE Trans. Pattern Anal. Machine Intell. 27, 178–193 (2005).

[CrossRef]

S. K. Nayar, X. S. Fang, and T. Boult, “Separation of reflection components using color and polarization,” Int. J. Comput. Vision 21, 163–186 (1997).

[CrossRef]

N. Keshava, “A survey of spectral unmixing algorithms,” Lincoln Lab. J. 14, 55–78 (2003).

P. Stigel, K. Miyata, and M. Hauta-Kasari, “Wiener estimation method in estimating of spectral reflectance from RGB images,” Pattern Recogn. Image Anal. 17, 233–242 (2007).

[CrossRef]

O. L. Frost III, “An algorithm for linearly constrained adaptive array processing,” Proc. IEEE 60, 926–935 (1972).

[CrossRef]

Q. Du, I. Kopriva, and H. Szu, “Investigation on constrained matrix factorization for hyperspectral image analysis,” in IEEE International Geoscience and Remote Sensing Symposium Proceedings (IEEE International, 2005), pp. 4304–4306.

R. H. Yuhas, A. F. H. Goetz, and J. W. Boardman, “Discrimination among semiarid landscape endmembers using the spectral angle mapper (SAM) algorithm,” in Summaries of the Third Annual JPL Airborne Geoscience Workshop, Publication 92-14 (Jet Propulsion Laboratory, 1992), Vol. 1, pp. 147–149.

http://people.cs.uu.nl/robby/textureSeparation/results.html (last viewed 02.9.2011).

P. Koirala, M. Hauta-Kasari, and J. Parkkinen, “Highlight removal from single image,” in Proceedings of Advanced Concepts for Intelligent Vision Systems (Springer, 2009), Vol. 5807, pp. 176–187.

P. Tan, S. Lin, L. Quan, and H-Y. Shum, “Highlight removal by illumination-constrained inpainting,” in Proceedings of the 9th IEEE International Conference on Computer Vision (IEEE Computer Society, 2003), pp. 164–169.

Z. Fu, R. T. Tan, and T. Caelli, “Specular free spectral imaging using orthogonal subspace projection,” in Proceedings of International Conference on Pattern Recognition (IEEE Computer Society, 2006), pp. 812–815.

V. Bochko and J. Parkkinen, “Highlight analysis using a mixture model of probabilistic PCA,” in Proceedings of the 4th WSEAS International Conference on Signal Processing, Robotics and Automation (World Scientific and Engineering Academy and Society, 2005), paper 15.

http://www.spectralcameras.com/files/downloads/VariSpec_Technote.pdf (last viewed 24.09.2010).

http://www.hyspex.no/products/hyspex/vnir1600.php (last viewed 03.07.2011).