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Found 5 papers, 1 papers with code
Matrix cofactorization for joint spatial-spectral unmixing of hyperspectral images
The most common way to introduce spatial information is to rely on a spatial regularization of the abundance maps.
Matrix Cofactorization for Joint Representation Learning and Supervised Classification -- Application to Hyperspectral Image Analysis
The attribution vectors of the clustering are then used as features vectors for the classification task, i. e., the coding vectors of the corresponding factorization problem.
Hyperspectral unmixing with spectral variability using adaptive bundles and double sparsity
The proposed method is designed to promote sparsity on the selection of both spectra and classes within each pixel.
Hierarchical Bayesian image analysis: from low-level modeling to robust supervised learning
Within a supervised classification framework, labeled data are used to learn classifier parameters.
Fast forward feature selection for the nonlinear classification of hyperspectral images
Experimental results for two real hyperspectral data sets show that the method performs very well in terms of classification accuracy and processing time.
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