Using Bayesian optimization to jointly tune the classifier and the random field for spatial-spectral hyperspectral classification. Gewali, U. B. & Monteiro, S. T. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017.
abstract   bibtex   
The framework consisting of a pixel-wise classification followed by a Markov random field has been very successful for spatial-spectral hyperspectral classification. While training such frameworks, the classifier and the Markov random field are generally tuned greedily one after another. However, better results could be obtained by tuning both of the components simultaneously with the objective of producing the best result at the end. This paper investigates the joint optimization of the hyperparameters of the classifier and the random field using Bayesian optimization. Experimental evaluation on the model comprising of a support vector machine classifier and a grid-structured Markov random field is provided. The results of the experiments, conducted on two independent datasets, suggest that the jointly tuned models can provide better accuracy.
@InProceedings{Gewali2017,
  author    = {Gewali, Utsav B. and Monteiro, Sildomar T.},
  title     = {{Using Bayesian optimization to jointly tune the classifier and the random field for spatial-spectral hyperspectral classification}},
  booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
  year      = {2017},
  abstract  = {The framework consisting of a pixel-wise classification followed by a Markov random field has been very successful for spatial-spectral hyperspectral classification. While training such frameworks, the classifier and the Markov random field are generally tuned greedily one after another. However, better results could be obtained by tuning both of the components simultaneously with the objective of producing the best result at the end. This paper investigates the joint optimization of the hyperparameters of the classifier and the random field using Bayesian optimization. Experimental evaluation on the model comprising of a support vector machine classifier and a grid-structured Markov random field is provided. The results of the experiments, conducted on two independent datasets, suggest that the jointly tuned models can provide better accuracy.},
}

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