The highly correlated spectral features and the limited training samples pose challenges in
hyperspectral image classification. In this article, to tackle the issues of end-to-end feature …

Recently, the field of hyperspectral image (HSI) classification is dominated by deep learning-
based methods. However, training deep learning models usually needs a large number of …

Transformer-based networks, which can well model the global characteristics of inputted
data using the attention mechanism, have been widely applied to hyperspectral image (HSI) …

Recently, a series of deep learning methods based on the convolutional neural networks
(CNNs) have been introduced for classification of hyperspectral images (HSIs). However, in …

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

The extraction of joint spatial-spectral features has been proved to improve the classification
performance of hyperspectral images (HSIs). Recently, utilizing convolutional neural …

In this article, we propose an end-to-end adaptive spectral-spatial multiscale network to
extract multiscale contextual information for hyperspectral image (HSI) classification, which …

Jointly using spatial and spectral information has been widely applied to hyperspectral
image (HSI) classification. Especially, convolutional neural networks (CNN) have gained …

Deep learning has recently attracted significant attention in the field of hyperspectral images
(HSIs) classification. However, the construction of an efficient deep neural network mostly …

Hyperspectral image (HSI) classification is an active and important research task driven by
many practical applications. To leverage deep learning models especially convolutional …