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 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 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 …

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 …

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 …

Purpose The research goal is to understand what factors affect users' knowledge and
information learning and sharing on social media platforms. This study focuses on the …

Low-rank methods have earned high regard for solving problems of mixed denoising in
hyperspectral images (HSI). However, for low-rank matrix/tensor-based denoising methods …

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

Deep learning (DL) has been widely applied to hyperspectral image (HSI) classification,
owing to its promising feature learning and representation capabilities. However, limited by …