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49(3):181–186.
Price, J. C. 1998. An approach for analysis of reflectance spectra. Remote Sensing of
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Psomas, A., Zimmermann, N. E., Kneubühler, M., Kellenberger, T., and Itten, K. 2005.
Seasonal variability in spectral reflectance for discriminating grasslands along a dry- mesic
gradient in Switzerland. Paper presented at the 4th EARSeL Workshop on Imaging
Spectroscopy. New Quality in Environmental Studies, 27–30 April, Warsaw, Poland.
Rao, N. R., Garg, P. K., and Ghosh, S. K. 2007. Development of an agricultural crops spectral
library and classification of crops at cultivar level using hyperspectral data. Precision
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Robson, C. 1994. Experiment, Design and Statistics in Psychology (3rd ed.). Penguin Books.
Salisbury, J. W. 1998. Spectral Measurements Field Guide. Defense Technical Information
Center.
Salvaggio, C., Smith, L. E., and Antoine, E. J. 2005. Spectral signature databases and their
application/misapplication to modeling and exploitation of multispectral/hyperspectral data.
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery
XI, Proceedings of SPIE, Vol. 5806, 531.
Sanchez-Hernandez, C., Boyd, D. S., and Foody, G. M. 2007. Mapping specific habitats from
remotely sensed imagery: Support vector machine and support vector data description based
classification of coastal saltmarsh habitats. Ecological Informatics 2:83–88.
Schaaf, C. B. 2009. Albedo and reflectance anisotropy: Assessment of the status of the
development of the standards for the Terrestrial Essential Climate Variables (Vol. 12).
Global Terrestrial Observing System. United National Framework Convention on Climate
Change, 30th ed. June 1–10, 2009, Bonn.
Schaepman, M. E. 2007. Spectrodirectional remote sensing: From pixels to processes.
International Journal of Applied Earth Observation and Geoinformation 9(2):
204–223.
Schmidt, K. S., and Skidmore, A. K. 2003. Spectral discrimination of vegetation types in a
coastal wetland. Remote Sensing of Environment 85(1):92–108.
Schopfer, J., Dangel, S., Kneubuhler, M., and Itten, K. I. 2008. The improved dual-view field
goniometer system FIGOS. Sensors 8(8):5120–5140.
Short, N. M. 2009. Remote Sensing Tutorial—Reference Publication 1078 Retrieved June 22,
2012, from http://rst.gsfc.nasa.gov/Front/tofc.html.
Small, C. 2001. Estimation of urban vegetation abundance by spectral mixture analysis.
International Journal of Remote Sensing 22:1305–1334.
Song, C., Woodcock, C. E., Seto, K. C., Lenney, M. P., and Macomber, S. A. 2001.
Classification and change detection using Landsat TM data: When and how to correct
atmospheric effects? Remote Sensing of Environment 75(2):230–244.
Styers, D. M., Chappelka, A. H., Marzen, L. J., and Somers, G. L. 2009. Developing a landcover classification to select indicators of forest ecosystem health in a rapidly urbanizing
landscape. Landsc. Urban Plan 94(3–4):158–165.
318
HYPERSPECTRAL REMOTE SENSING WITH EMPHASIS ON LAND COVER MAPPING
network-based characterisation of mixed pixels in remotely sensed hyperspectral images.
Pattern Recognition 42:3032–3045.
Price, J. C. 1994. How unique are spectral signatures? Remote Sensing of Environment
49(3):181–186.
Price, J. C. 1998. An approach for analysis of reflectance spectra. Remote Sensing of
Environment 64(3):316–330.
Psomas, A., Zimmermann, N. E., Kneubühler, M., Kellenberger, T., and Itten, K. 2005.
Seasonal variability in spectral reflectance for discriminating grasslands along a dry- mesic
gradient in Switzerland. Paper presented at the 4th EARSeL Workshop on Imaging
Spectroscopy. New Quality in Environmental Studies, 27–30 April, Warsaw, Poland.
Rao, N. R., Garg, P. K., and Ghosh, S. K. 2007. Development of an agricultural crops spectral
library and classification of crops at cultivar level using hyperspectral data. Precision
Agriculture 8(4–5):173–185.
Robson, C. 1994. Experiment, Design and Statistics in Psychology (3rd ed.). Penguin Books.
Salisbury, J. W. 1998. Spectral Measurements Field Guide. Defense Technical Information
Center.
Salvaggio, C., Smith, L. E., and Antoine, E. J. 2005. Spectral signature databases and their
application/misapplication to modeling and exploitation of multispectral/hyperspectral data.
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery
XI, Proceedings of SPIE, Vol. 5806, 531.
Sanchez-Hernandez, C., Boyd, D. S., and Foody, G. M. 2007. Mapping specific habitats from
remotely sensed imagery: Support vector machine and support vector data description based
classification of coastal saltmarsh habitats. Ecological Informatics 2:83–88.
Schaaf, C. B. 2009. Albedo and reflectance anisotropy: Assessment of the status of the
development of the standards for the Terrestrial Essential Climate Variables (Vol. 12).
Global Terrestrial Observing System. United National Framework Convention on Climate
Change, 30th ed. June 1–10, 2009, Bonn.
Schaepman, M. E. 2007. Spectrodirectional remote sensing: From pixels to processes.
International Journal of Applied Earth Observation and Geoinformation 9(2):
204–223.
Schmidt, K. S., and Skidmore, A. K. 2003. Spectral discrimination of vegetation types in a
coastal wetland. Remote Sensing of Environment 85(1):92–108.
Schopfer, J., Dangel, S., Kneubuhler, M., and Itten, K. I. 2008. The improved dual-view field
goniometer system FIGOS. Sensors 8(8):5120–5140.
Short, N. M. 2009. Remote Sensing Tutorial—Reference Publication 1078 Retrieved June 22,
2012, from http://rst.gsfc.nasa.gov/Front/tofc.html.
Small, C. 2001. Estimation of urban vegetation abundance by spectral mixture analysis.
International Journal of Remote Sensing 22:1305–1334.
Song, C., Woodcock, C. E., Seto, K. C., Lenney, M. P., and Macomber, S. A. 2001.
Classification and change detection using Landsat TM data: When and how to correct
atmospheric effects? Remote Sensing of Environment 75(2):230–244.
Styers, D. M., Chappelka, A. H., Marzen, L. J., and Somers, G. L. 2009. Developing a landcover classification to select indicators of forest ecosystem health in a rapidly urbanizing
landscape. Landsc. Urban Plan 94(3–4):158–165.
318
HYPERSPECTRAL REMOTE SENSING WITH EMPHASIS ON LAND COVER MAPPING