In today's digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning
(DL), are widely used for various computer vision tasks such as image classification, object …

Convolutional neural networks (CNNs) and vision transformers (ViTs) have shown excellent
capability in complex hyperspectral image (HSI) classification. However, these models …

Hyperspectral image (HSI) classification plays a crucial role in remote sensing (RS)
applications, enabling the precise identification of materials and land cover based on …

Deep learning relies on learning from extensive data to generate prediction results. This
approach may inadvertently capture spurious correlations within the data, leading to models …

Convolutional neural networks (CNNs), in particular, demonstrate the remarkable power of
feature learning in remote sensing for land use and cover classification, as demonstrated by …

Integrating metaheuristic algorithms and optimization techniques with remote sensing
technology has accelerated the advent of advanced methodologies for analyzing …

The complexity of urban road scenes at night and the inadequacy of visible light imaging in
such conditions pose significant challenges. To address the issues of insufficient color …

The classifiers based on the convolutional neural network (CNN) and graph convolutional
network (GCN) have demonstrated their effectiveness in hyperspectral image (HSI) …

Global attention on the exploration of hyperspectral imaging (HSI) system for non-destructive
evaluation of detailed analyses of food and agricultural products has increased significantly …

Transformers have shown remarkable success in modeling sequential data and capturing
intricate patterns over long distances. Their self-attention mechanism allows for efficient …