Deep learning (DL) based methods represented by convolutional neural networks (CNNs)
are widely used in hyperspectral image classification (HSIC). Some of these methods have …

Practical applications ask for object detection models that achieve high performance at low
overhead. Knowledge distillation demonstrates favorable potential in this case by …

Advanced Hyperspectral image sensors can capture high-resolution land cover images.
Many supervised Machine learning (ML) and Deep learning (DL) algorithms succeeded in …

Deep learning (DL) techniques have shown remarkable progress in remotely sensed
hyperspectral image (HSI) classification tasks. The performance of DL-based models highly …

The remarkable growth of deep learning (DL) algorithms in hyperspectral images (HSIs) in
recent years has garnered a lot of research space. This study examines and analyses over …

Deep learning-based hyperspectral image (HSI) classification has attracted more and more
attention because of its excellent classification ability. Generally, the outstanding …

Hyperspectral image (HSI) classification has attracted significant interest among researchers
owing to its diverse practical applications. Convolutional neural networks (CNNs) have been …

Hyperspectral imaging is highly important with respect to the detection, identification and
classification of various natural resources—minerals, earth's natural eruptions, vegetation …

Semantic segmentation of very-high-resolution (VHR) remote sensing images plays an
important role in the intelligent interpretation of remote sensing since it predicts pixel-level …

Recently, deep learning methods based on the combination of spatial and spectral features
have been successfully applied in hyperspectral image (HSI) classification. To improve the …