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Hyperspectral Image Classification Using Gaussian Mixture Models and Markov Random Fields
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DOI：10.1109/LGRS.2013.2250905.png)
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The Gaussian mixture model is a well-known classification tool that captures non-Gaussian statistics of multivariate data. However, the impractically large size of the resulting parameter space has hindered widespread adoption of Gaussian mixture models for hyperspectral imagery. To counter this parameter-space issue, dimensionality reduction targeting the preservation of multimodal structures is proposed. Specifically, locality-preserving nonnegative matrix factorization, as well as local Fisher's discriminant analysis, is deployed as preprocessing to reduce the dimensionality of data for the Gaussian-mixture-model classifier, while preserving multimodal structures within the data. In addition, the pixel-wise classification results from the Gaussian mixture model are combined with spatial-context information resulting from a Markov random field. Experimental results demonstrate that the proposed classification system significantly outperforms other approaches even under limited training data.
Keyword:
Gaussian mixture model (GMM)
hyperspectral classification
Markov random field (MRF)
nonnegative matrix factorization
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
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