The construction of diverse dictionaries for sparse representation of hyperspectral image
(HSI) classification has been a hot topic over the past few years. However, compared with …

In recent studies in hyperspectral imaging, biometrics, and energy analytics, the framework
of deep dictionary learning has shown promise. Deep dictionary learning outperforms other …

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 …

Sparse representation models a signal as a linear combination of a small number of
dictionary atoms. As a generative model, it requires the dictionary to be highly redundant in …

Sparse representation and dictionary learning methods have been successfully applied in
classification of hyperspectral images (HSIs). However, when the number of training data is …

Dictionary learning has yielded impressive results in sparse representation based
hyperspectral image (HSI) classification. However, challenges remain for exploiting spectral …

Sparse representation of signals for classification is an active research area. Signals can
potentially have a compact representation as a linear combination of atoms in an …

Abundant spectral information of hyperspectral images (HSIs) has shown an obvious
advantage in improving the performance of classification in the remote sensing domain …

In this letter, we propose a new sparse representation (SR)-based method for hyperspectral
image (HSI) classification, namely SR with incremental dictionaries (SRID). Our SRID boosts …

Low-rank and sparse representation (LRSR) model has gained popularity in hyperspectral
image (HSI) classification. However, most existing LRSR models are limited by the highly …