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Data-Driven Compressive Sampling and Learning Sparse Coding for Hyperspectral Image Classification
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DOI：10.1109/LGRS.2013.2268847.png)
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Exploring the sparsity in classifying hyperspectral vectors proves to lead to state-of-the-art performance. To learn a compact and discriminative dictionary for accurate and fast classification of hyperspectral images, a data-driven Compressive Sampling (CS) and learning sparse coding scheme are use to reduce the dimensionality and size of the dictionary respectively. First, a sparse radial basis function (RBF) kernel learning network (S-RBFKLN) is constructed to learn a compact dictionary for sparsely representing hyperspectral vectors. Then a data-driven compressive sampling scheme is designed to reduce the dimensionality of the dictionary, and labels of new samples are derived from coding coefficients. Some experiments are taken on NASA EO-1 Hyperion data and AVIRIS Indian Pines data to investigate the performance of the proposed method, and the results show its superiority to its counterparts.
Keyword:
Compressive sampling (CS)
data-driven
hyperspectral image classification
sparse radial basis function kernel learning network (S-RBFKLN)
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