Advances in computing technology have fostered the development of new and powerful
deep learning (DL) techniques, which have demonstrated promising results in a wide range …

Lately, with deep learning outpacing the other machine learning techniques in classifying
images, we have witnessed a growing interest of the remote sensing community in …

We present a novel hyperspectral image reconstruction algorithm, which overcomes the
long-standing tradeoff between spectral accuracy and spatial resolution in existing …

With the rapid development of deep learning technology and improvement in computing
capability, deep learning has been widely used in the field of hyperspectral image (HSI) …

Hyperspectral image (HSI) classification has attracted wide attention in many fields.
Applying Graph Neural Network (GNN) to HSI classification is one of the research frontiers …

Recently, the graph convolutional network (GCN) has drawn increasing attention in the
hyperspectral image (HSI) classification. Compared with the convolutional neural network …

Hyperspectral imaging (HSI) has been extensively utilized in many real-life applications
because it benefits from the detailed spectral information contained in each pixel. Notably …

Hyperspectral imaging is a non-destructive, nonpolluting, and fast technology, which can
capture up to several hundred images of different wavelengths and offer relevant spectral …

In recent years, deep-learning techniques revolutionized the way remote sensing data are
processed. The classification of hyperspectral data is no exception to the rule, but it has …

In recent years, deep-learning-based hyperspectral image (HSI) classification methods have
achieved significant development. The superior capability of feature extraction from these …