Satellite imageries are an important geoinformation source for different applications in the
Earth Science field. However, due to the limitation of the optic and sensor technologies and …

Convolutional neural networks have made a great breakthrough in recent remote sensing
image super-resolution (SR) tasks. Most of these methods adopt upsampling layers at the …

Satellite video cameras can provide continuous observation for a large-scale area, which is
important for many remote sensing applications. However, achieving moving object …

Deep learning (DL)-based compressed sensing (CS) has been applied for better
performance of image reconstruction than traditional CS methods. However, most existing …

With the advances of deep learning, many recent CNN-based methods have yielded
promising results for image classification. In very high-resolution (VHR) remote sensing …

In the field of remote sensing, due to memory consumption and computational burden, the
single-image super-resolution (SISR) methods based on deep convolution neural networks …

Single-image super-resolution (SISR) is an important task in image processing, which aims
to enhance the resolution of imaging systems. Recently, SISR has made a huge leap and …

Deep learning–based hyperspectral image super-resolution (SR) methods have achieved
remarkable success, which can improve the spatial resolution of hyperspectral images with …

With the rapid development of deep convolutional neural networks (CNNs), super-resolution
(SR) in hyperspectral image (HSI) has achieved good results. Current methods generally …

The deep learning based super-resolution (SR) methods have recently achieved
remarkable progress in the reconstruction of ideally simulated high-quality remote sensing …