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field spectroscopy. Remote Sensing of Environment 113:S92–S109.
Morrey, J. R. 1968. On determining spectral peak positions from composite spectra with a
digital computer. Analytical Chemistry 40(6):905–914.
Mutanga, O., Skidmore, A. K., and van Wieren, S. 2003. Discriminating tropical grass
(Cenchrus ciliaris) canopies grown under different nitrogen treatments using spectroradiometry. ISPRS Journal of Photogrammetry and Remote Sensing 57(4):263–272.
National Institute of Standards and Technology (NIST) 2003. E-Handbook of Statistical
Methods: Tukey’s Method. Retrieved June, 2010, from http://www.itl.nist.gov/div898/
handbook/prc/section4/prc471.htm.
Nicodemus, F. F., Richmond, J. C., Hsia, J. J., Ginsberg, I. W., and Limperis, T. L. 1977.
Geometrical Considerations and Nomenclature for Reflectance (Vol. Monograph 160)
Washington, DC: National Bureau of Standards.
Okin, G. S., Roberts, D. A., Murray, B., and Okin, W. J. 2001. Practical limits on hyperspectral
vegetation discrimination in arid and semiarid environments. Remote Sensing of Environment 77(2):212–225.
Otukei, J. R., and Blaschke, T. 2010. Land cover change assessment using decision trees,
support vector machines and maximum likelihood classification algorithms. International
Journal of Applied Earth Observation and Geoinformation S12:S27–S31.
Pal, M. 2006. Support vector machine-based feature selection for land cover classification: A
case study with DAIS hyperspectral data. International Journal of Remote Sensing
27(14):2877–2894.
Pal, M., and Mather, P. M. 2005. Some issues in the classification of DAIS hyperspectral data.
International Journal of Remote Sensing 27:2895–2916.
Pal, N. R., and Pal, S. K. 1993. A review of image segmentation techniques. Pattern
Recognition 26:1277–1294.
Peddle, D. R., White, H. P., Soffer, R. J., Miller, J. R., and LeDrew, E. F. 2001. Reflectance
processing of remote sensing spectroradiometer data. Computers & Geosciences 27(2):
203–213.
Petropoulos, G. P., Kalaitzidis, C., and Vadrevu, K. P. 2011. Support Vector machines and
object-based classification for obtaining land use/cover cartography from hyperion hyperspectral imagery. Computers and Geosciences 41:99–107.
Petropoulos, G. P., Arvanitis, K., and Sigrimis, N. 2012a. Hyperion hyperspectral imagery
analysis combined with machine learning classifiers for land use/cover mapping. Expert
Systems with Applications 39:3800–3809.
Petropoulos, G. P., Vadrevu, K. P., and Kalaitzidis, C. 2012b. Spectral Angle mapper and
object-based classification combined with hyperspectral remote sensing imagery for
obtaining land use/cover mapping in a mediterranean region. Geocarto International
28(2):1–16.
Pfitzner, K., Bollh, F. A., and Carr, G. 2006. A standard design for collecting vegetation
reference spectra: Implementation and implications for data sharing. Journal of Spatial
Science 51(2):79–92.
Pignatti, S., Cavalli, R. M., Cuomo, V., Fusilli, V., Pascucci, S., Poscolieri, M., and Santini, F.
2009. Evaluating hyperion capability for land cover mapping in a fragmented ecosystem:
Pollino National Park, Italy. Remote Sensing of Environment 113:622–634.
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317