Deep learning (DL) has been extensively used for hyperspectral image classification (HSIC)
with significant success, but the classification of high-dimensional hyperspectral image (HSI) …

Deep learning-based methods have produced significant gains for hyperspectral image
(HSI) classification in recent years, leading to high impact academic achievements and …

Benefiting from powerful feature extraction capabilities, convolutional neural networks
(CNNs) have gained prominence in hyperspectral image (HSI) classification. Nevertheless …

More recently, vision transformer (ViT) has shown competitive performance with
convolutional neural network (CNN) on computer vision tasks, which provided more …

Cross-scene hyperspectral image classification (HSIC) is a challenging topic in remote
sensing, especially when there are no labels in the target domain. Domain adaptation (DA) …

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 …

Convolutional neural network (CNN) has achieved great success in hyperspectral image
(HSI) classification. However, the local receptive field of CNNs leads to the drawback in …

Recently, hyperspectral image classification (HIC) with noisy labels is attracting increasing
interest. However, existing methods usually neglect to explore feature-dependent …

The label noise is a serious problem limiting the performance of federated learning.
According to the performance evaluation for the trained federated models, data selection …

Nowadays, HSI classification can reach a high classification accuracy when given sufficient
labeled samples as training set. However, the performances of existing methods decrease …