Hyperspectral image (HSI) classification has become a hot topic in the field of remote
sensing. In general, the complex characteristics of hyperspectral data make the accurate …

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 …

In hyperspectral image (HSI) classification, each pixel sample is assigned to a land-cover
category. In the recent past, convolutional neural network (CNN)-based HSI classification …

Hyperspectral image (HSI) classification is widely used for the analysis of remotely sensed
images. Hyperspectral imagery includes varying bands of images. Convolutional neural …

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 images (HSIs) provide rich spectral–spatial information with stacked
hundreds of contiguous narrowbands. Due to the existence of noise and band correlation …

Vision transformers (ViTs) have been trending in image classification tasks due to their
promising performance when compared with convolutional neural networks (CNNs). As a …

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 …

In recent years, convolutional neural networks (CNNs) have drawn significant attention for
the classification of hyperspectral images (HSIs). Due to their self-attention mechanism, the …

Hyperspectral image (HSI) classification aims to assign each hyperspectral pixel with a
proper land-cover label. Recently, convolutional neural networks (CNNs) have shown …