This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
W. Boardman, F. Kruse, and R. Green, “Mapping target signatures via partial unmixing of AVIRIS data,” in VIJPL airborne Earth Science Workshop, Pasadena, CA, 1995.
M. Winter, “N-FINDR: An algorithm for fast autonomous spectral end-member determination in hyperspectral data,” in Proc. SPIE, vol. 3753, 1999, pp. 266–275.
R. Neville, K. Staenz, T. Szeredi, and P. Hauff, “Automatic endmember extraction from hyperspectral data for mineral exploration,” in 21st Can. Symp. Remote Sensing, Ottawa, ON, Canada, 1999.
A. Plaza, P. Martinez, R. Perez, and J. Plaza, “Spatial/spectral endmember extraction by multidimensional morphological operations,” IEEE transactions on geoscience and remote sensing, vol. 40, pp. 2025–2041, Sept. 2002.
A. Ifarraguerri and C.-I. Chang, “Multispectral and hyperspectral image analysis with convex cones,” IEEE transactions on geoscience and remote sensing, vol. 37, pp. 756–770, Mar. 1999.
M. Lennon, G. Mercier, M.-C. Mouchot, and L. Hubert-Moy, “Spectral unmixing of hyperspectral images with the Independent Component Analysis and wavelet packets,” in IGARSS, vol. 6, July 1999, pp. 2896–2898.
C. J. Burges, “A tutorial on support vector machines for pattern recognition,” in Data mining and knowledge discovery, U. Fayyad, Ed. Kluwer Academic, 1998, pp. 1–3.
V. N. Vapnick, Statistical Learning Theory. John Wiley and Sons Inc., 1998.
C. Huang, L. S. Davis, and J. R. G. Townshend, “An assessement of support vector machines for land cover classifiocation,” Int. J. Remote sensing, vol. 23, no. 4, pp. 725–749, 2002.
J. A. Gualtieri and R. F. Cromp, “Support vector machines for hyperspectral remote sensing classification,” in Proceedings of the SPIE, vol. 3584, 1999, pp. 221–232.
F. Melgani and L. Bruzzone, “Support vector machines for classification of hyperspectral remote sensing images,” in IGARSS, 2002.
M. Lennon, G. Mercier, and L. Hubert-Moy, “Classification of hyperspectral images with nonlinear filtering and support vector machines,” in IGARSS, 2002.
G. Hughes, “On the mean accuracy of statistical pattern recognition,” IEEE transactions on information theory, vol. 14, no. 1, pp. 55–63, 1968.
G. Smits and E. Jordaan, “Improved SVM regression using mixtures of kernels,” in IJCNN, 2002.
G. Mercier and M. Lennon, “Support Vector Machines for hyperspectral image classification with spectral-based kernels,” in IGARSS, 2003.
A. Gersho and R. Gray, Vector Quantization and Signal Compression. Klower Academic Publisher, 1992.
M. Ryan and J. Arnold, “The lossless compression of AVIRIS images by vector quantization,” IEEE transactions on geoscience and remote sensing, vol. 35, no. 3, pp. 546–550, May 1997.
S.-E. Qian, A. Hollinger, D. Williams, and D. Manak, “3D data compression of hyperspectral imagery using vector quantization with NDVI-based multiple codebooks,” in IGARSS, 1998.
A. Czihò, B. Solaiman, G. Cazuguel, C. Roux, and I. Loványi, “Kohonen’s self organizing feature maps with variable learning rate. application to image compression,” in 3rdinternational workshop on Image and Signal Processing, Santorini, Greece, 1997.
T. Kohonen, “The self-organizing map,” Proceedings of the IEEE, vol. 78, no. 9, pp. 1464–1480, September 1990.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer Science+Business Media, Inc.
About this chapter
Cite this chapter
Mercier, G., Lennon, M. (2006). Joint Classification and Compression of Hyperspectral Images. In: Motta, G., Rizzo, F., Storer, J.A. (eds) Hyperspectral Data Compression. Springer, Boston, MA. https://doi.org/10.1007/0-387-28600-4_7
Download citation
DOI: https://doi.org/10.1007/0-387-28600-4_7
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-28579-5
Online ISBN: 978-0-387-28600-6
eBook Packages: Computer ScienceComputer Science (R0)