Graph convolutional networks (GCNs) are a promising approach for addressing the
necessity for long-range information in hyperspectral image (HSI) classification …

More recently, vision transformer (ViT) has shown competitive performance with
convolutional neural network (CNN) on computer vision tasks, which provided more …

Hyperspectral image classification (HSIC) is a rapidly developing field that utilizes deep
learning methods. However, the reliance on convolutional neural networks (CNNs) for …

At present, the existing Vision Transformer is sensitive to the original spectral data in the
hyperspectral semantic segmentation process. Moreover, the weight relationship between …

Convolutional neural networks (CNNs) are widely used in hyperspectral image classification
(HSIC) due to their exceptional performance. However, current methods for multiscale …

It is a crucial means for humans to perceive geomorphic features and landscape
architectures by classifying ground objects in hyperspectral images (HSIs). Currently, the …

As a promising model compression technique, knowledge distillation aims to supervise the
training of small networks with advanced knowledge from large networks to improve the …

Abstract Recent classifications in Earth Observation (EO) commonly involve a combination
of Hyperspectral Image (HSI) and Light Detection and Ranging (LiDAR) signals. However …

Deep learning has been utilized for hyperspectral image (HSI) classification in recent years,
with notable performance improvements. In particular, convolutional neural networks …

Hyperspectral image classification is crucial for a wide range of applications, including
environmental monitoring, precision agriculture, and mining, due to its ability to capture …