Ссылки
Chang, C. I. (2000). An information theoretic-based approach to spectral variability, similarity and discriminability for hyperspectral image analysis. IEEE Transaction on Information Theory, 46(5), 1927-1932.
Chang, C. I. (2003). Hyperspectral imaging: Techniques for spectral detection and classification. New York: Kluwer Academic/Plenum Publishers.
Chang, C. I. & Plaza, A. (2006). A fast iterative algorithm for implementation of pixel purity index. IEEE Transaction on Geoscience and Remote Sensing, 3(1), 63-67.
Clark, R. N., & Roush, T. L. (1984). Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications. Journal of Geophysical Research, 89, 6329-6340.
Cover, Thomas M., & Thomas, Joy A. (2006). Elements of information theory (2nd ed.). Amsterdam: Wiley Publications.
Du, H., Chang, C. I., Ren, H., D'amico, F. M., & Jensen, J. O. (2004). New hyperspectral discrimination measure for spectral characterization. Optical Engineering, 43(8), 1777-1786.
Divvedi, R. S., Kandrika, Sreenivas, & Ramana, K. V. (2003). Comparison of classifiers of remote-sensing data for land-use/land-cover mapping. Current Science, 86, 328-335.
Fleiss, J. L., Cohen, J., & Everitt, B. S. (1969). Large-sample standard errors of kappa and weighted kappa. *Psychology Bulletin*, 72, 323-327.
Fung, T., & Ledrew, E. (1988). The determination of optimal threshold levels for change detection using various accuracy indices. *Photogrammetric Engineering & Remote Sensing*, 54, 1449-1454.
Goncalves, R. P., Assis, L. C., & Vieira, C. A. O. (2007). Comparison of sampling methods to classify of remotely sensed images. In *IV International Symposium in Precision in Agriculture*, 23-25 October, Vicosa.
Jiang, X., Tang, L., Wang, C., & Wang, C. (2004). Spectral characteristics and feature selection of hyperspectral remote sensing data. *International Journal of Remote Sensing*, 25(1), 51-59.
Kong, X., Shu, N., Huang, W., & Fu, J. (2010). The research effectiveness of spectral similarity measures for hyperspectral image. In *Presented in 3rd International Congress on Image and Signal Processing (CISP2010)*.
Kruse, F. A. (2008). Comparison of ATERM, ACORN, and FLASSH atmospheric corrections using low altitude AVIRIS data of Boulder, Co, USA. *September 21, 2008. http://www.hgimaging.com/FA_Pubs.htm*.
Kullback, S. (1977). *Information theory and statistics*. MA: Dover Gloucester.
Okin, G. S., Roberts, D. A., Murray, B., & Okin, W. J. (2000). Practical limits on hyperspectral vegetation discrimination in arid and semiarid regions. *Remote Sensing of Environment*, 77(2), 212-225.
Pearlman, J. S., Barry, P. S., Segal, C. C., Shepanski, J., Beiso, D., & Carman, S. L. (2003). Hyperion, a spaceborne imaging spectrometer. *IEEE Transactions on Geoscience and Remote Sensing*, 41(6), 1160-1173.
Roberts, D. A., Adams, J. B., & Smith, M. O. (1993). Discriminating green vegetation, non-photosynthetic vegetation and soil in AVIRIS data. *Remote Sensing of Environment*, 44(3), 255-270.
T sai, F. L., Lin, E.-K., & Yoshino, K. (2007). Spectrally segmented principal component analysis of hyperspectral imagery for invasive plant species mapping. *International Journal of Remote Sensing*, 28(5-6), 1023-1039.
Van der Meer, F. (2005). The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery. *International Journal of Remote Sensing*, 26(1), 1-15.
Journal of Applied Earth Observation and Geoinformation.
https://doi.org/10.1016/j.jag.2005.06.001.
Weng, Q. (2011). Advances in environmental remote sensing: sensors, algorithms and applications, chapter 5 (pp. 118–120).and chapter 20 (pp.513–523). Boca Raton: CRC Press, Taylor & Francis.