Hyperspectral image classification (HSIC) methods based on convolutional neural network
(CNN) continue to progress in recent years. However, high complexity, information …

To improve the accuracy and generalization ability of hyperspectral image classification, a
feature extraction method integrating principal component analysis (PCA) and local binary …

Hyperspectral image (HSI) provided with a substantial number of correlated bands causes
calculation consumption and an undesirable “dimension disaster” problem for the …

In recent years, due to its powerful feature extraction ability, the deep learning method has
been widely used in hyperspectral image classification tasks. However, the features …

Hyperspectral image classification (HSIC) faces three major challenging issues, which are
generally overlooked. One is how to address the background (BKG) issue due to its …

Convolutional neural network (CNN) has received considerable interest in hyperspectral
image classification (HSIC) lately due to its excellent spectral–spatial feature extraction …

One fundamental task of hyperspectral imaging is spectral unmixing. In this case, the
conventional pure pixel-based hyperspectral image classification (HSIC) may not work …

In hyperspectral image classification (HSIC), background (BKG) is generally excluded from
consideration due to the fact that obtaining complete knowledge of BKG is nearly impossible …

Although hyperspectral technology has continued to improve over the years, it is still limited
to size, weight, and power (SWaP) constraints. One major issue is the need to sample a …

Multifarious hyperspectral image (HSI) classification methods based on convolutional neural
networks (CNN) have been gradually proposed and achieve a promising classification …