Unsupervised Hyperspectral Image Analysis Using Independent Component Analysis ( ICA )

@inproceedings{Chiang2000UnsupervisedHI,
  title={Unsupervised Hyperspectral Image Analysis Using Independent Component Analysis ( ICA )},
  author={Shao-Shan Chiang and C. Chang and Irving W. Ginsberg},
  year={2000}
}
In this paper, an ICA-based approach is proposed for hyperspectral image analysis. It can be viewed as a random version of the commonly used linear spectral mixture analysis, in which the abundance fractions in a linear mixture model are considered to be unknown independent signal sources. It does not require the full rank of the separating matrix or orthogonality as most ICA methods do. More importantly, the learning algorithm is designed based on the independency of the material abundance… CONTINUE READING

Citations

Publications citing this paper.
Showing 1-10 of 33 extracted citations

References

Publications referenced by this paper.
Showing 1-5 of 5 references

A noise subspace projection approach to determination of intrinsic dimensionality for hyperspectral imagery,

C.-I Chang, Q. Du
EOS/SPIE Symposium on Remote Sensing, Conference on Image and Signal Processing for Remote Sensing V, SPIE vol. 3871, • 1999
View 1 Excerpt

Independent component analysis for remote sensing study,

C. H. Chen, X. Zhang
EOS/SPIE Symposium on Remote Sensing, Conference on Image and Signal Processing for Remote Sensing V, SPIE vol. 3871, • 1999
View 1 Excerpt

Indpendent Component Analysis: Theory and Applications

T. W. Lee
Boston: Kluwer Academic Publishers • 1998
View 1 Excerpt

Independent component analysis, A new concept?

Signal Processing • 1994
View 2 Excerpts

Image spectroscopy: interpretation based on spectral mixture analysis," Remote Geochemical Analysis: Elemental and Mineralogical Composition, edited by C.M

J. B. Adams, M. O. Smith, A. R. Gillespie
Pieters and P.A. Englert, • 1993
View 1 Excerpt

Similar Papers

Loading similar papers…