Learning Sparse CRFs for Feature Selection and Classification of Hyperspectral Imagery

@article{Zhong2008LearningSC,
  title={Learning Sparse CRFs for Feature Selection and Classification of Hyperspectral Imagery},
  author={Ping Zhong and Runsheng Wang},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2008},
  volume={46},
  pages={4186-4197}
}
Feature selection is an important task in hyperspectral data analysis. This paper presents a sparse conditional random field (SCRF) model to select relevant features for the classification of hyperspectral images and, meanwhile, to exploit the contextual information in the form of spatial dependences in the images. The sparsity arises from the use of a Laplacian prior on the CRF parameters, which encourages the parameter estimates to be either significantly large or exactly zero. To joint the… CONTINUE READING
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