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 …

Deep learning-based models have been shown to outperform human beings in many
computer vision tasks with massive available labeled training data in learning. However …

In recent years, forest-fire monitoring methods represented by deep learning have been
developed rapidly. The use of drone technology and optimization of existing models to …

Recently, deep learning models have achieved excellent performance in hyperspectral
image (HSI) classification. Among the many deep models, Transformer has gradually …

In order to achieve accurate hyperspectral image (HSI) classification, the convolutional
neural network (CNN) has been extensively utilized. However, most existing patch-based …

In the convolutional neural network, the precise segmentation of small-scale objects and
object boundaries in remote sensing images is a great challenge. As the model gets deeper …

In the wake of developments in remote sensing, the application of target detection of remote
sensing is of increasing interest. Unfortunately, unlike natural image processing, remote …

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 …

The application of digital technology in teaching has triggered the evolution of traditional
teaching. Students have different corresponding relationships under digital behavior. The …

Hyperspectral image (HSI) classification refers to accurately corresponding each pixel in an
HSI to a land-cover label. Recently, the successful application of multiscale and multifeature …