Spatially Adaptive Semi-supervised Learning with Gaussian Processes for Hyperspectral Data Analysis

@article{Jun2010SpatiallyAS,
  title={Spatially Adaptive Semi-supervised Learning with Gaussian Processes for Hyperspectral Data Analysis},
  author={Goo Jun and Joydeep Ghosh},
  journal={Statistical Analysis and Data Mining},
  year={2010},
  volume={4},
  pages={358-371}
}
This paper presents a semi-supervised learning algorithm called Gaussian process expectation-maximization (GPEM), for classification of landcover based on hyperspectral data analysis. Model parameters for each land cover class are first estimated by a supervised algorithm using Gaussian process regressions to find spatially adaptive parameters, and the estimated parameters are then used to initialize a spatially adaptive mixture-of-Gaussians model. The mixture model is updated by expectation… CONTINUE READING

Citations

Publications citing this paper.

References

Publications referenced by this paper.
Showing 1-10 of 37 references

The use of geographically weighted regression for spatial prediction: an evaluation of models using simulated data sets

  • P. Harris, A. Fotheringham, R. Crespo, M. Charlton
  • Math Geosci 42(6)
  • 2010
1 Excerpt

Similar Papers

Loading similar papers…