[HTML][HTML] Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion
Optical remote sensing imagery is at the core of many Earth observation activities. The
regular, consistent and global-scale nature of the satellite data is exploited in many …
regular, consistent and global-scale nature of the satellite data is exploited in many …
[HTML][HTML] Production of global daily seamless data cubes and quantification of global land cover change from 1985 to 2020-iMap World 1.0
Longer time high-resolution, high-frequency, consistent, and more detailed land cover data
are urgently needed in order to achieve sustainable development goals on food security …
are urgently needed in order to achieve sustainable development goals on food security …
Cloud detection in remote sensing images based on multiscale features-convolutional neural network
Z Shao, Y Pan, C Diao, J Cai - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Cloud detection in remote sensing images is a challenging but significant task. Due to the
variety and complexity of underlying surfaces, most of the current cloud detection methods …
variety and complexity of underlying surfaces, most of the current cloud detection methods …
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 …
Thin cloud removal in optical remote sensing images based on generative adversarial networks and physical model of cloud distortion
Cloud contamination is an inevitable problem in optical remote sensing images. Unlike thick
clouds, thin clouds do not completely block out background which makes it possible to …
clouds, thin clouds do not completely block out background which makes it possible to …
Cloud removal in remote sensing images using nonnegative matrix factorization and error correction
In the imaging process of optical remote sensing platforms, clouds are an inevitable barrier
to the effective observation of sensors. To recover the original information covered by the …
to the effective observation of sensors. To recover the original information covered by the …
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 …
Simultaneous cloud detection and removal from bitemporal remote sensing images using cascade convolutional neural networks
Clouds and cloud shadows heavily affect the quality of the remote sensing images and their
application potential. Algorithms have been developed for detecting, removing, and …
application potential. Algorithms have been developed for detecting, removing, and …
[图书][B] Multisensor data fusion and machine learning for environmental remote sensing
In the last few years the scientific community has realized that obtaining a better
understanding of interactions between natural systems and the man-made environment …
understanding of interactions between natural systems and the man-made environment …
Thin cloud removal with residual symmetrical concatenation network
Thin cloud removal is important for enhancing the utilization of optical remote sensing
imagery. Different from thick cloud removal, the pixels contaminated by thin clouds still …
imagery. Different from thick cloud removal, the pixels contaminated by thin clouds still …