A hyperspectral imaging sensor collects detailed spectral responses from ground objects
using hundreds of narrow bands; this technology is used in many real-world applications …

Hyperspectral image (HSI) classification is an important task in earth observation missions.
Convolution neural networks (CNNs) with the powerful ability of feature extraction have …

Hyperspectral imaging has become a mature technology which brings exciting possibilities
in various domains, including satellite image analysis. However, the high dimensionality and …

The use of deep learning methods in hyperspectral image (HSI) classification has been a
promising approach due to its powerful ability to automatically extract features in recent …

Numerous state-of-the-art transformer-based techniques with self-attention mechanisms
have recently been demonstrated to be quite effective in the classification of hyperspectral …

Accurate hyperspectral image classification has been an important yet challenging task for
years. With the recent success of deep learning in various tasks, 2-dimensional (2D)/3 …

Hyperspectral image analysis has been gaining research attention thanks to the current
advances in sensor design which have made acquiring such imagery much more affordable …

Hyperspectral sensors acquire spectral responses from objects with a large number of
narrow spectral bands. The large volume of data may be costly in terms of storage and …

Hyperspectral image (HSI) classification is a classical task in remote sensing image
analysis. With the development of deep learning, schemes based on deep learning have …

The timely and accurate prediction of winter wheat yields is of importance in maintaining
food security. However, existing deep-learning methods used for crop yield prediction are …