Machine learning, particularly deep learning (DL), has become a central and state-of-the-art
method for several computer vision applications and remote sensing (RS) image …

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

Neural networks have dominated the research of hyperspectral image classification,
attributing to the feature learning capacity of convolution operations. However, the fixed …

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

Three-dimensional object detection from light detection and ranging (LiDAR) point clouds
has gained great attention in recent years due to its wide applications in smart cities and …

Hyperspectral image (HSI) classification is one of the most important tasks in hyperspectral
data analysis. Convolutional neural networks (CNN) have been introduced to HSI …

For hyperspectral image (HSI) classification, two branch networks generally use
convolutional neural networks (CNNs) to extract the spatial features and long short-term …

In deep learning-based hyperspectral remote sensing image classification tasks, random
sampling strategies are typically used to train model parameters for testing and evaluation …