In hyperspectral image (HSI) classification, each pixel sample is assigned to a land-cover
category. In the recent past, convolutional neural network (CNN)-based HSI classification …

In our preliminary study, the reflectance signatures obtained from hyperspectral imaging
(HSI) of normal and abnormal corneal epithelium tissues of porcine show similar …

Convolutional neural networks (CNNs) are widely used for hyperspectral image (HSI)
classification due to their better ability to model the local details of HSI. However, CNNs …

In recent years, convolutional neural networks (CNNs) have achieved great success in
hyperspectral image (HSI) classification tasks. CNNs focus more on the local features of …

For the spatial‐spectral classification of hyperspectral images (HSIs), a deep learning
framework is proposed in this study, which consists of convolutional neural networks (CNNs) …

The unique characteristics of hyperspectral images (HSI) undoubtedly pose significant
categorization issues while providing a wealth of information. High redundancy and a lack of …

SIGNIFICANCE: Advances in imaging technology over the last two decades have produced
significant innovations in medical imaging. Hyperspectral imaging (HSI) is one of these …

In recent years, transformer has attracted the attention of many researchers in the field of
remote sensing due to its ability to model global information. However, it is difficult to extract …

Convolutional neural networks (CNNs) have been widely used in hyperspectral image
classification due to their ability to extract image features effectively. However, under the …

Classifying hyperspectral images in remote sensing applications is challenging due to
limited training samples and high dimensionality of data. Deep-learning-based methods …