Data-Driven Compressive Sampling and Learning Sparse Coding for Hyperspectral Image Classification | IEEE Journals & Magazine | IEEE Xplore

Data-Driven Compressive Sampling and Learning Sparse Coding for Hyperspectral Image Classification


Abstract:

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 ...Show More

Abstract:

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.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 11, Issue: 2, February 2014)
Page(s): 479 - 483
Date of Publication: 15 August 2013

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