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  1. J.R. Jensen, Remote Sensing of Environment: An Earth resource perspective, Pearson Education, 2003.
  2. G. Camara, A.M.V. Monteiro, J. Paiva, and R. C. M. Souza, “Action-Driven Ontologies of the Geographical Space. In: M.J. Egenhofer and D.M. Mark (Editors),” GIScience, AAG, Savannah, GA, 2000.
  3. U. C. Benz, P. Hofmann, G. Willhauck, I. Lingenfelder, and M. Heynen, “Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information,” Photogrammetry and Remote Sensing, vol. 58, pp. 239-258, 2004.
  4. H. Sun, S. Li, W. Li, Z. Ming, and S. Cai, “Semantic-Based Retrieval of Remote Sensing Images in a Grid Environment,” IEEE Geoscience and Remote Sensing Letters, vol. 2 (4), pp. 440-444, 2005.
  5. EEA & ETC/LC, Corine LC Technical Guide, 1999. Available: http://etc.satellus.se/the_data/Technical_Guide/index.htm
  6. M. Torma, and P. Harma, “Accuracy of CORINE LC Classification in Northern Finland,” Geoscience and Remote Sensing Symposium, 2004. IGARSS '04, Proceedings, 2004 IEEE International, vol. 1, pp. 227-230, 20-24 Sept. 2004.
  7. Natural Resources Census, “National Landuse and LC Mapping Using Multitemporal AWiFS Data (LULC – AWiFS),” Project Manual, Remote Sensing & GIS Applications Area, National Remote Sensing Agency, Department of Space, Government of India, Hyderabad, April 2005.
  8. P. Bosdogianni, M. Petrou, and K. Josef, “Mixture Models with Higher Order Moments,” IEEE Transaction on Geoscience and Remote Sensing, vol. 35(2), pp. 341-353, 1997.
  9. Y. J. Kaufman, and D. Tanre, “Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS,” IEEE Transaction on Geoscience and Remote Sensing, vol. 30 (2), pp. 261-270, 1992.
  10. A. Strahler, D. Muchoney, J. Borak, M. Fried, S. Gopal, E. Lambin, and A. Moody, “MODIS LC Product, Algorithm Theoretical Basis Document (ATBD) Version 5.0, MODIS LC and Land-Cover Change,” 1999.
  11. A. D. Stocker, I. S. Reed, X. Yu, “Multi-dimensional signal processing for electrooptical target detection,” Proc. SPIE Internatinal Society for Optical Eng, vol. 1305, pp. 1-7, 1990.
  12. R. H. Yuhas, A. F. H. Goetz, and J. W. Boardman, “Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm,” Summaries 3rd Annual JPL Airborne geoscience Workshop, vol. 1, pp. 147–149, 1992.
  13. J. T. Kent, and K. V. Mardia, “Spectral classification using fuzzy membership models,” IEEE Trans. Pattern Anal. Machine Intell, vol. 10(4), pp. 659–671, 1986.
  14. J. B. Adams, M. O. Smith, and P. E. Johnson, “Spectral mixture modelling: A new analysis of rock and soil types at the Viking Lander 1 site,” Journal of Geophysical Research, vol. 91, pp. 8098 – 8112, 1986.
  15. J. J. Settle, N. A. Drak, “Linear mixing and the estimation of ground cover proportion,” International Journal of Remote Sensing, vol. 14 (6), pp. 1159–1177, 1993.
  16. J. Boardman, “Analysis, understanding and visualization of hyperspectral data as a convex set in n-space,” International SPIE symposium on Imaging Spectrometry, Orlando, Florida, pp. 23-36, 1995.
  17. H. M. Horwitz, R.F. Nalepka, P. D. Hyde, and J.P. Morgenstern, “Estimating the proportions of objects within a single pixel resolution elements of a multispectral scanner,” In Proc, 7th Int. Symp, Remote Sensing of Environment (Ann Arbor, MI), pp. 1307-1320, 1971.
  18. D. M. Detchmendy, and W.H. Pace, “A model for spectral signature variability for mixtures,” Remote Sensing of Earth Resources, vol 1 F, Shahrokhi, Ed. Tullahoma TN: Univ. Temmessee Press, pp.596-620, 1972.
  19. Y. E. Shimabukuro, and A. J. Smith, “The least-squares mixing models to generate fraction images derived from remote sensing multispectral data,” IEEE Transactions on Geoscience and Remote Sensing,” vol. 29(1), pp. 16–20, 1991.
  20. M. E. Winter, and E. M. Winter, “Comparison of Approaches for Determining End-members in Hyperspectral Data,” IEEE Transaction on Geoscience and Remote Sensing, vol 3, pp. 305-313, 2000.
  21. C. A. Bateson, G. P. Asner, and C. A. Wessman, “Endmember Bundles: A New Approach to Incorporating Endmember Variability into Spectral Mixture Analysis,” IEEE Transactions on Geoscience and Remote Sensing, vol. 38 (2), pp. 1083-1094, 2000.
  22. A. Plaza, P. Martinez, R. Perez, and J. Plaza, “A Quantitative and Comparative Analysis of Endmember Extraction Algorithms From Hyperspectral Data,” IEEE Transactions on Geoscience and Remote Sensing, vol. 42(3), pp. 650-663, 2004.
  23. J. Settle, “On the effect of Variable Endmember Spectra in the Linear Mixture Model,” IEEE Transaction on Geoscience and Remote Sensing, vol. 44 (2), pp. 389-396, 2006.
  24. L.R. Inverson, E. A. Cook, and R. L. Graham, “A technique for extrapolation and validating forest cover across large regions: calibrating AVHRR data with TM data,” International Journal of Remote Sensing, vol. 10, pp. 1805-1812, 1989.
  25. N. Quarmbay, J. Townshend, J. Settle, K. White, M. Milnes, T. Hindle, and N. Silleos, “Linear mixture modelling applies to AVHRR data for crop area estimation,” International Journal of Remote Sensing, vol. 13(3), pp. 415-425, 1992.
  26. C. Palaniswami, A. K. Upadhyay, and H. P. Maheswarappa, “Spectral mixture analysis for subpixel classification of coconut,” Current Science, vol. 91(12), pp. 1706-1711, 2006.
  27. A. M. Cross, J. J. Settle, N. A. Drake, and R. T. M. Paivinin, “Subpixel measurement of tropical forest cover using AVHRR data,” International Journal of Remote Sensing, vol. 12(5), pp. 1119-1129, 1991.
  28. http://modis.gsfc.nasa.gov/data/dataprod/
  29. J. A. C. Ballantine, G. S. Okin, D. E. Prentiss, and D. A. Roberts, “Mapping North African landforms using continental scale unmixing of MODIS imagery,” Remote Sensing of Environment, vol. 97 (4), pp. 470-483, 2005.
  30. B. H. Braswell, S. C. Hagen, S. E. Frolking, W. A. Salas, “A multivariable approach for mapping sub-pixel LC distributions using MISR and MODIS: Application in the Brazilian Amazon region,” Remote Sensing of Environment, vol. 87 (2-3), pp. 243-256, 2003.
  31. http://edcimswww.cr.usgs.gov/pub/imswelcome/
  32. T. V. Ramachandra and G. R. Rao, “Inventorying, mapping and monitoring of bioresources using GIS and remote sensing,” Geospatial Technology for Developmental Planning, Allied Publishers Pvt. Ltd. New Delhi., pp: 49-76, 2005.
  33. T.V. Ramachandra and Uttam Kumar, “Geographic resources decision support system for land use, land cover dynamics analysis,” Proceedings of the FOSS/GRASS Users Conference - Bangkok, Thailand, .12-14 Sept 2004. http://gisws.media.osaka-cu.ac.jp/grass04/papers.php?first_letter=T
  34. L. Zhu, and R. Tateishi, “Application of Linear Mixture Model to tile series AVHRR data,” Paper presented at the 22nd Asian Conference on Remote Sensing, Singapore, 5 – 9 November, 2001.
  35. M. M. Dundar, and D. Landgrebe, “A Model-Based Mixture-Supervised Classification Approach in Hyperspectral Data Analysis,” IEEE Transaction on Geoscience and Remote Sensing, vol. 40 (12), pp. 2692-2699, 2002.
  36. R. Tateishi, Y. Shimazaki, and P. D. Gunin, “Spectral and temporal linear mixing model for vegetation classification,” International Journal of Remote Sensing, vol. 25(20), pp. 4208-4218, 2004.
  37. F. Maselli, “Multiclass spectral decomposition of remotely sensed scenes by selective pixel unmixing,” IEEE Transaction on Geoscience and Remote Sensing, vol. 36 (5), pp. 1809-1820, 2002.
  38. Z. Wang, D. Ziou, C. Armenakis, D. Li, and Q. Li, “A Comparative Analysis of Image Fusion Methods,” IEEE Transactions of Geoscience and Remote Sensing, vol. 43 (6), pp. 1391-1402, 2005.