The remarkable growth of deep learning (DL) algorithms in hyperspectral images (HSIs) in
recent years has garnered a lot of research space. This study examines and analyses over …

In recent years, pixel-wise hyperspectral image (HSI) classification has received growing
attention in the field of remote sensing. Plenty of spectral–spatial convolutional neural …

Hyperspectral image classification (HSIC) based on deep learning has always been a
research hot spot in the field of remote sensing. However, most of the classification models …

Recently, hyperspectral image (HSI) classification has become a promising research
direction in remote sensing image processing. Many HSI classification methods have been …

Over last decade, hundreds of deep learning algorithms using CNN, ViT, MLP, and deep
LSTM are proposed to classify hyperspectral remote sensing images with accuracy reaching …

To address the issues of inadequate feature expression capacity and poor adaptability of
feature fusion in traditional hyperspectral image classification methods, a new approach to …

The helical phasefront and orbital angular momentum (OAM) of vortex electromagnetic
waves (VEMW) have attracted extensive attention in expanding communication capacity and …

The approach based on Convolutional Neural Network model has been widely employed in
the field of hyperspectral image classification, demonstrating promising classification …

摘要针对高光谱影像波段间相关度强, 光谱和空间结构复杂性高和训练样本数量有限等问题,
提出一种边缘保护滤波和深度残差网络结合的分类方法. 首先采用联合双边滤波增强地物的边缘 …