Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …

Feature selection has become an indispensable machine learning process for data
preprocessing due to the ever-increasing sizes in actual data. There have been many …

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 a critical task with numerous applications in the
field of remote sensing. Although convolutional neural networks have achieved remarkable …

Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN), such as Graph
Attention Networks (GAT), are two classic neural network models, which are applied to the …

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

Deep learning, which is a subfield of machine learning, has opened a new era for the
development of neural networks. The auto-encoder is a key component of deep structure …

The key elements that underpin food security require the adaptation of agricultural systems
to support productivity increases while minimizing inputs and the adverse effects of climate …

Due to its impressive representation power, the graph convolutional network (GCN) has
attracted increasing attention in the hyperspectral image (HSI) classification. However, the …

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