Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …

Advances in computing technology have fostered the development of new and powerful
deep learning (DL) techniques, which have demonstrated promising results in a wide range …

Remote sensing image scene classification is one of the most challenging problems in
understanding high-resolution remote sensing images. Deep learning techniques …

Over the years, advances in sensor technologies have enhanced spatial, temporal, spectral,
and radiometric resolutions, thus significantly improving the size, resolution, and quality of …

Hyperspectral imaging (HSI) is a powerful tool that can capture and analyze a range of
spectral bands, providing unparalleled levels of precision and accuracy in data analysis …

Deep neural networks (DNNs), including convolutional neural networks (CNNs) and
residual networks (ResNets) models, are able to learn abstract representations from the …

The increase in the spectral and spatial information of hyperspectral imagery poses
challenges in classification due to the fact that spectral bands are highly correlated, training …

Recently, deep learning-based hyperspectral image (HSI) classification techniques are
flourishing and exhibit good performance, where cross-domain information is usually utilized …

In recent years, deep convolutional neural networks (CNNs) have been widely used for
hyperspectral image (HSI) classification. The powerful feature extraction capability and high …

The primary source of food is extracted from the plant. Take care of and maintain the plants
in real-time to enhance human survival. Diseases in plants can directly lead to a reduction in …