Recently, many efforts have been concentrated on land use land cover (LULC) classification
due to rapid urbanization, environmental pollution, agriculture drought, frequent floods, and …

Image dehazing is a representative low-level vision task that estimates latent haze-free
images from hazy images. In recent years, convolutional neural network-based methods …

Due to the limitation of optical images that their waves cannot penetrate clouds, such images
always suffer from cloud contamination, which causes missing information and limitations for …

Clouds and haze often occlude optical satellite images, hindering continuous, dense
monitoring of the Earth's surface. Although modern deep learning methods can implicitly …

Because of the presence of clouds, the available information in optical remote sensing
images is greatly reduced. These temporal‐based methods are widely used for cloud …

Optical remote sensing images are often contaminated by clouds and shadows, resulting in
missing data, which greatly hinders consistent Earth observation missions. Cloud and …

Cloud contamination is a common issue that severely reduces the quality of optical satellite
images in remote sensing fields. With the rapid development of deep learning technology …

Due to the all-weather and all-time imaging capability of synthetic aperture radar (SAR),
SAR remote sensing analysis has attracted much attention recently. However, compared …

The existence of thick clouds covers the comprehensive Earth observation of optical remote
sensing images (RSIs). Cloud removal is an effective and economical preprocessing step to …

Cloud contamination greatly limits the potential utilization of optical images for geoscience
applications. An effective alternative is to extract data from synthetic aperture radar (SAR) …