Abstract-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-mixturemodel 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. Index Terms-Gaussian mixture model (GMM), hyperspectral classification, Markov random field (MRF), nonnegative matrix factorization.
In article REF , the dimensionality reduction targeting the preservation of multimodal structures was proposed to counter this parameterspace issue.
Hyperspectral Image Classification Using Gaussian Mixture Models and Markov Random Fields
IEEE Geoscience and Remote Sensing Letters
IEEE Geoscience and Remote Sensing Letters
Mathematics Computer Science