Cloud and cloud shadow detection for optical satellite imagery: Features, algorithms, validation, and prospects
The presence of clouds prevents optical satellite imaging systems from obtaining useful
Earth observation information and negatively affects the processing and application of …
Earth observation information and negatively affects the processing and application of …
Water body classification from high-resolution optical remote sensing imagery: Achievements and perspectives
Water body classification from high-resolution optical remote sensing (RS) images, aiming at
classifying whether each pixel of the image is water or not, has become a hot issue in the …
classifying whether each pixel of the image is water or not, has become a hot issue in the …
Thick cloud and cloud shadow removal in multitemporal imagery using progressively spatio-temporal patch group deep learning
Thick cloud and its shadow severely reduce the data usability of optical satellite remote
sensing data. Although many approaches have been presented for cloud and cloud shadow …
sensing data. Although many approaches have been presented for cloud and cloud shadow …
Attention mechanism-based generative adversarial networks for cloud removal in Landsat images
The existence of clouds affects the quality of optical remote sensing images. Cloud removal
is an important preprocessing procedure to effectively improve the utilization of optical …
is an important preprocessing procedure to effectively improve the utilization of optical …
Cloud removal with fusion of high resolution optical and SAR images using generative adversarial networks
The existence of clouds is one of the main factors that contributes to missing information in
optical remote sensing images, restricting their further applications for Earth observation, so …
optical remote sensing images, restricting their further applications for Earth observation, so …
Combined deep prior with low-rank tensor SVD for thick cloud removal in multitemporal images
The thick cloud coverage phenomenon severely disturbs optical satellite observation
missions (covering approximately 40–60% areas in the global scale). Therefore, the manner …
missions (covering approximately 40–60% areas in the global scale). Therefore, the manner …
Thick cloud removal in Landsat images based on autoregression of Landsat time-series data
Thick-cloud contamination causes serious missing data in Landsat images, which
substantially limits applications of these images. To remove thick clouds from Landsat data …
substantially limits applications of these images. To remove thick clouds from Landsat data …
Denoising diffusion probabilistic feature-based network for cloud removal in Sentinel-2 imagery
R Jing, F Duan, F Lu, M Zhang, W Zhao - Remote Sensing, 2023 - mdpi.com
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 …
images in remote sensing fields. With the rapid development of deep learning technology …
[HTML][HTML] Efficacy of the SDGSAT-1 glimmer imagery in measuring sustainable development goal indicators 7.1. 1, 11.5. 2, and target 7.3
Abstract The Sustainable Development Goals Satellite 1 (SDGSAT-1), equipped with the
Glimmer Imager (GLI), provides high-resolution nighttime light (NTL) data across multiple …
Glimmer Imager (GLI), provides high-resolution nighttime light (NTL) data across multiple …
Thick cloud removal under land cover changes using multisource satellite imagery and a spatiotemporal attention network
H Liu, B Huang, J Cai - IEEE Transactions on Geoscience and …, 2023 - ieeexplore.ieee.org
Remote sensing satellites provide observations of the Earth's surface, which are crucial data
for applications and analyses in several fields, including agriculture, environmental …
for applications and analyses in several fields, including agriculture, environmental …