Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …

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 …

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 …

Hyperspectral (HS) images are characterized by approximately contiguous spectral
information, enabling the fine identification of materials by capturing subtle spectral …

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

Recently, a great many deep convolutional neural network (CNN)-based methods have
been proposed for hyperspectral image (HSI) classification. Although the proposed CNN …

Hyperspectral image (HSI) consists of abundant spectral and spatial characteristics, which
contribute to a more accurate identification of materials and land covers. However, most …

Hyperspectral image (HSI) classification is widely used for the analysis of remotely sensed
images. Hyperspectral imagery includes varying bands of images. Convolutional neural …

In recent years, researchers have paid increasing attention on hyperspectral image (HSI)
classification using deep learning methods. To improve the accuracy and reduce the training …

Hyperspectral image (HSI) classification has become a hot topic in the field of remote
sensing. In general, the complex characteristics of hyperspectral data make the accurate …