Recently, networks consider spectral-spatial information in multiscale inputs less, even
though there are some networks that consider this factor, however these networks cannot …

Hyperspectral image (HSI) classification accuracy has been greatly improved by employing
deep learning. The current research mainly focuses on how to build a deep network to …

Deep spectral-spatial features fusion has become a research focus in hyperspectral image
(HSI) classification. However, how to extract more robust spectral-spatial features is still a …

Hyperspectral image classification (HSIC) is one of the most important research topics in the
field of remote sensing. However, it is difficult to label hyperspectral data, which limits the …

In recent years, the deep learning-based hyperspectral image (HSI) classification method
has achieved great success, and the convolutional neural network (CNN) method has …

Hyperspectral images possess a continuous and analogous spectral nature, enabling the
classification of distinctive information by analyzing the subtle variations between adjacent …

Extracting spatial and spectral features through deep neural networks has become an
effective means of classification of hyperspectral images. However, most networks rarely …

The convolutional neural network (CNN) can automatically extract hierarchical feature
representations from raw data and has recently achieved great success in the classification …

Compared with traditional hyperspectral image classification methods, the classification
model based on the deep convolutional neural network (DCNN) can achieve higher …

Hyperspectral images (HSIs) have been playing an important role in the field of ground
object classification because of their rich spatial and spectral information. Aiming at how to …