Deep learning algorithms have shown significant advantages in hyperspectral image (HSI)
classification. However, these algorithms usually require a large number of labeled samples …

Deep convolutional neural networks (CNNs) have shown their outstanding performance in
the hyperspectral image (HSI) classification. The success of CNN-based HSI classification …

Hyperspectral images (HSIs) are rich in spatial and spectral information, making them
crucial for accurate classification. While existing convolutional neural network (CNN) …

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 learning models, especially deep convolutional neural networks (CNNs), have been
intensively investigated for hyperspectral image (HSI) classification due to their powerful …

Recently, hyperspectral image (HSI) classification methods based on convolutional neural
networks (CNN) have shown impressive performance. However, HSI classification still faces …

Feature representation is crucial for hyperspectral image (HSI) classification. However,
existing convolutional neural network (CNN)-based methods are limited by the convolution …

This paper studies the classification problem of hyperspectral image (HSI). Inspired by the
great success of deep neural networks in Artificial Intelligence (AI), researchers have …

Due to the powerful feature information mining ability of deep learning, models such as
convolutional neural network (CNN) and Transformer have gained a certain progress in …

Convolutional neural networks (CNN) are becoming increasingly popular in modern remote
sensing image classification tasks and have exhibited excellent results. For the existing …