Spectral–Spatial Classification of Hyperspectral Data Based on a Stochastic Minimum Spanning Forest Approach

@article{Bernard2012SpectralSpatialCO,
  title={Spectral–Spatial Classification of Hyperspectral Data Based on a Stochastic Minimum Spanning Forest Approach},
  author={K{\'e}vin Bernard and Yuliya Tarabalka and Jes{\'u}s Angulo and Jocelyn Chanussot and Jon Atli Benediktsson},
  journal={IEEE Transactions on Image Processing},
  year={2012},
  volume={21},
  pages={2008-2021}
}
In this paper, a new method for supervised hyperspectral data classification is proposed. In particular, the notion of stochastic minimum spanning forest (MSF) is introduced. For a given hyperspectral image, a pixelwise classification is first performed. From this classification map, M marker maps are generated by randomly selecting pixels and labeling them as markers for the construction of MSFs. The next step consists in building an MSF from each of the M marker maps. Finally, all the M… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 66 CITATIONS, ESTIMATED 38% COVERAGE

66 Citations

051015'13'15'17'19
Citations per Year
Semantic Scholar estimates that this publication has 66 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
SHOWING 1-10 OF 55 REFERENCES

An overview of morphological segmentation

  • F. Meyer
  • Int. J. Pattern Recog. Artif. Intell., vol. 15…
  • 2001
Highly Influential
4 Excerpts

Similar Papers

Loading similar papers…