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Original Articles

Hyperspectral Image Classification Based on Quadratic Fisher's Discriminant Analysis and Multi-class Support Vector Machine

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REFERENCES

  • AVIRIS, airborneimager data. Available: http://aviris.jpl.nasa.gov/data/index.html
  • D. Landgrebe, “Hyperspectral image data analysis,” Signal Processing Magazine, IEEE, Vol. 19, no. 1, pp. 17–28, Jan. 2002.
  • P. K. Varshney, and M. K. Arora, Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data. Berlin Heidelberg: Springer-Verlag, 2004.
  • P. Dong, and J. Liu, “Hyperspectral image classification using support vector machines with an efficient principal component analysis scheme,” in Y. Wang and T. Li (Eds.) Foundations of Intelligent Systems, Advances in Intelligent and Soft Computing, Vol. 122, Berlin: Springer, 2012, pp. 131–40.
  • A. Villa, J. Benediktsson, J. Chanussot, and C. Jutten, “Hyperspectral image classification with independent component discriminant analysis,” IEEE Trans. Geosci. Remote Sens., Vol. 49, no. 12, pp. 4865–76, 2011.
  • A.M. Martinez, and A. Kak, “Pca versus lda,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 23, no. 2, 228–33, 2001.
  • G. Camps-Valls, D. Tuia, L. Bruzzone, and J. Atli Benediktsson, “Advances in hyperspectral image classification: Earth monitoring with statistical learning methods,” IEEE Trans. Signal Process., Vol. 31, no. 1, pp. 45–54, 2014.
  • M. Rojas, I. Do´pido, A. Plaza, and P. Gamba, “Comparison of support vector machine-based processing chains for hyperspectral image classification,” in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 7810, 2010.
  • Q. Gu, Z. Li, and J. Han, “Linear discriminant dimensionality reduction,” in D. Gunopulos, T. Hofmann, D. Malerba, M. Vazirgiannis (eds.) Machine Learning and Knowledge Discovery in Databases, Lecture Notes in Computer Science, Vol. 6911, Berlin: Springer, 2011, pp. 549–64.
  • R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification. New York, NY: John Wiley & Sons, 2001, pp. 117–24. ISBN: 0-471-05669-3
  • V. S. Devi, and M. N. Murty, Pattern Recognition An Introduction. Universities Press, India, 2011, pp. 147–83. ISBN: 978-81-7371-725-3
  • F. Melgani, and L. Bruzzone, “Classification of hyperspectral remote sensing images with support vector machines,” IEEE Trans. Geosci. Remote Sens., Vol. 42, no. 8, 1778–90, 2004.
  • G. Mercier, and M. Lennon, “Support vector machines for hyperspectral image classification with spectral-based kernels,” in Proceeding of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Vol. 1, Toulouse, France, July 21–25, 2003, pp. 288–90.
  • C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Min. Knowl. Discovery, Vol. 2, no. 2, pp. 121–67, 1998.

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