Combining rich spectral and spatial information, a hyperspectral image (HSI) can provide a
more comprehensive characterization of the Earth's surface. To better exploit HSIs, a large …

Recent imaging science and technology discoveries have considered hyperspectral
imagery and remote sensing. The current intelligent technologies, such as support vector …

Hyperspectral (HS) images are characterized by approximately contiguous spectral
information, enabling the fine identification of materials by capturing subtle spectral …

Convolutional neural networks (CNNs) have been attracting increasing attention in
hyperspectral (HS) image classification due to their ability to capture spatial-spectral feature …

The application of graph convolutional networks (GCNs) to hyperspectral image (HSI)
classification is a heavily researched topic. However, GCNs are based on spectral filters …

Deep learning (DL) is an efficient method for botnet attack detection. However, the volume of
network traffic data and memory space required is usually large. It is, therefore, almost …

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

Due to its various application potentials, the remote sensing image scene classification
(RSSC) has attracted a broad range of interests. While the deep convolutional neural …

Hyperspectral image (HSI) contains complex multiple structures. Therefore, the key problem
analyzing the intrinsic properties of an HSI is how to represent the structure relationships of …

Hyperspectral image (HSI) classification aims to identify categories of hyperspectral pixels.
Recently, many convolutional neural networks (CNNs) have been designed to explore the …