The Regularized Iteratively Reweighted MAD Method for Change Detection in Multi- and Hyperspectral Data

@article{Nielsen2007TheRI,
  title={The Regularized Iteratively Reweighted MAD Method for Change Detection in Multi- and Hyperspectral Data},
  author={Allan Aasbjerg Nielsen},
  journal={IEEE Transactions on Image Processing},
  year={2007},
  volume={16},
  pages={463-478}
}
This paper describes new extensions to the previously published multivariate alteration detection (MAD) method for change detection in bi-temporal, multi- and hypervariate data such as remote sensing imagery. Much like boosting methods often applied in data mining work, the iteratively reweighted (IR) MAD method in a series of iterations places increasing focus on "difficult" observations, here observations whose change status over time is uncertain. The MAD method is based on the established… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 208 CITATIONS, ESTIMATED 31% COVERAGE

208 Citations

0102030'09'12'15'18
Citations per Year
Semantic Scholar estimates that this publication has 208 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
SHOWING 1-10 OF 50 REFERENCES

Robust Statistics. New York: Wiley, 1981

  • P. J. Huber
  • IEEE TRANSACTIONS ON IMAGE PROCESSING,
  • 2007
1 Excerpt

Analysis of multi-temporal remote sensing images

  • R. L. King, N. H. Younan, Eds.
  • MultiTemp Conf., Biloxi, MS, May 16–18, .
  • 2005
1 Excerpt

Regularisation in multi- and hyperspectral remote sensing change detection

  • A. A. Nielsen
  • presented at the 6th Geomatic Week Conf…
  • 2005
1 Excerpt

Similar Papers

Loading similar papers…