Unsupervised classification of hyperspectral images by using linear unmixing algorithm

@article{Luo2009UnsupervisedCO,
  title={Unsupervised classification of hyperspectral images by using linear unmixing algorithm},
  author={Bin Luo and Jocelyn Chanussot},
  journal={2009 16th IEEE International Conference on Image Processing (ICIP)},
  year={2009},
  pages={2877-2880}
}
In this paper, we present an unsupervised classification algorithm for hyperspectral images. For reducing the dimension of hyperspectral data, we use a linear unmixing algorithm to extract the endmembers and their abundance maps. Compared to the components obtained by traditional PCA-basedmethod, the abundancemaps have physical meanings (such as the abundance of vegetation). For determining the number of endmembers contained in an image, we propose an eigenvalue based approach. The validation… CONTINUE READING
11 Citations
6 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 11 extracted citations

References

Publications referenced by this paper.
Showing 1-6 of 6 references

A criterion for determining the number of groups in a data set using sum of squares clustering

  • W. J. Krzanowski, Y. T. Lai
  • Biometrics, vol. 44, pp. 23–34, 1988.
  • 1988
Highly Influential
3 Excerpts

An introduction to multivariate statistical analysis, Springer-Verlag

  • T. W. Anderson
  • 2nd edition,
  • 1984
2 Excerpts

Similar Papers

Loading similar papers…