Understanding GANs: fundamentals, variants, training challenges, applications, and open problems
Generative adversarial networks (GANs), a novel framework for training generative models
in an adversarial setup, have attracted significant attention in recent years. The two …
in an adversarial setup, have attracted significant attention in recent years. The two …
[PDF][PDF] An overview on the generation and detection of synthetic and manipulated satellite images
Due to the reduction of technological costs and the increase of satellite launches, satellite
images are becoming more popular and easier to obtain. Besides serving benevolent …
images are becoming more popular and easier to obtain. Besides serving benevolent …
UnCRtainTS: Uncertainty quantification for cloud removal in optical satellite time series
Clouds and haze often occlude optical satellite images, hindering continuous, dense
monitoring of the Earth's surface. Although modern deep learning methods can implicitly …
monitoring of the Earth's surface. Although modern deep learning methods can implicitly …
A unified framework of cloud detection and removal based on low-rank and group sparse regularizations for multitemporal multispectral images
The existing cloud removal methods either need a cloud mask as prior knowledge or detect
clouds before cloud removal processing, ie, the detection and removal processes are …
clouds before cloud removal processing, ie, the detection and removal processes are …
Generative adversarial learning in YUV color space for thin cloud removal on satellite imagery
X Wen, Z Pan, Y Hu, J Liu - Remote Sensing, 2021 - mdpi.com
Clouds are one of the most serious disturbances when using satellite imagery for ground
observations. The semi-translucent nature of thin clouds provides the possibility of 2D …
observations. The semi-translucent nature of thin clouds provides the possibility of 2D …
Thin cloud removal fusing full spectral and spatial features for Sentinel-2 imagery
J Li, Y Zhang, Q Sheng, Z Wu, B Wang… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
Multispectral remote sensing images are widely used for monitoring the globe. Although thin
clouds can affect all optical bands, the influences of thin clouds differ with band wavelength …
clouds can affect all optical bands, the influences of thin clouds differ with band wavelength …
[HTML][HTML] Deep learning based thin cloud removal fusing vegetation red edge and short wave infrared spectral information for Sentinel-2A imagery
Thin clouds seriously affect the availability of optical remote sensing images, especially in
visible bands. Short-wave infrared (SWIR) bands are less influenced by thin clouds, but …
visible bands. Short-wave infrared (SWIR) bands are less influenced by thin clouds, but …
Cloudformer: A cloud-removal network combining self-attention mechanism and convolution
P Wu, Z Pan, H Tang, Y Hu - Remote Sensing, 2022 - mdpi.com
Optical remote-sensing images have a wide range of applications, but they are often
obscured by clouds, which affects subsequent analysis. Therefore, cloud removal becomes …
obscured by clouds, which affects subsequent analysis. Therefore, cloud removal becomes …
Cloud-egan: Rethinking cyclegan from a feature enhancement perspective for cloud removal by combining cnn and transformer
Cloud cover presents a major challenge for geoscience research of remote sensing images
with thick clouds causing complete obstruction with information loss while thin clouds …
with thick clouds causing complete obstruction with information loss while thin clouds …
Deriving non-cloud contaminated sentinel-2 images with RGB and near-infrared bands from sentinel-1 images based on a conditional generative adversarial network
Sentinel-2 images have been widely used in studying land surface phenomena and
processes, but they inevitably suffer from cloud contamination. To solve this critical optical …
processes, but they inevitably suffer from cloud contamination. To solve this critical optical …