Hyperspectral sensor adaptability in precision agriculture to digital images is still at its
nascent stage. Hyperspectral imaging (HSI) is data rich in solving agricultural problems like …

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 …

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

Hyperspectral imaging (HSI) has been extensively utilized in many real-life applications
because it benefits from the detailed spectral information contained in each pixel. Notably …

Deep learning (DL) has been extensively used for hyperspectral image classification (HSIC)
with significant success, but the classification of high-dimensional hyperspectral image (HSI) …

Efficient and automated data acquisition techniques, as well as intelligent and accurate data
processing and analysis techniques, are essential for the advancement of precision …

Hyperspectral image classification (HSIC) has become a hot research topic. Hyperspectral
imaging (HSI) has been widely used in a wide range of real-world application areas due to …

Despite the success of deep learning algorithms in hyperspectral image (HSI) classification,
most deep learning models require a large amount of labeled data to optimize the numerous …

Carbon cycle is influenced by agricultural soils, and accurately map** the soil organic
carbon (SOC) content of global Mollisols at a 30 m spatial resolution can contribute to …

Rapid technological advances in airborne hyperspectral and lidar systems paved the way
for using machine learning algorithms to map urban environments. Both hyperspectral and …