scholarly journals Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets

2017 ◽  
Author(s):  
Kenji Enomoto ◽  
Ken Sakurada ◽  
Weimin Wang ◽  
Hiroshi Fukui ◽  
Masashi Matsuoka ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Cloud Removal

scholarly journals Generative Adversarial Learning in YUV Color Space for Thin Cloud Removal on Satellite Imagery

2021 ◽  
Vol 13 (6) ◽  
pp. 1079
Author(s):  
Xue Wen ◽  
Zongxu Pan ◽  
Yuxin Hu ◽  
Jiayin Liu
Keyword(s):  
Transfer Learning ◽  
Satellite Imagery ◽  
Network Architecture ◽  
Satellite Image ◽  
Color Space ◽  
Training Data ◽  
Training Strategy ◽  
Thin Cloud ◽  
Cloud Removal ◽  
Thin Cloud Removal

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 ground scene reconstruction based on a single satellite image. In this paper, we propose an effective framework for thin cloud removal involving two aspects: a network architecture and a training strategy. For the network architecture, a Wasserstein generative adversarial network (WGAN) in YUV color space called YUV-GAN is proposed. Unlike most existing approaches in RGB color space, our method performs end-to-end thin cloud removal by learning luminance and chroma components independently, which is efficient at reducing the number of unrecoverable bright and dark pixels. To preserve more detailed features, the generator adopts a residual encoding–decoding network without down-sampling and up-sampling layers, which effectively competes with a residual discriminator, encouraging the accuracy of scene identification. For the training strategy, a transfer-learning-based method was applied. Instead of using either simulated or scarce real data to train the deep network, adequate simulated pairs were used to train the YUV-GAN at first. Then, pre-trained convolutional layers were optimized by real pairs to encourage the applicability of the model to real cloudy images. Qualitative and quantitative results on RICE1 and Sentinel-2A datasets confirmed that our YUV-GAN achieved state-of-the-art performance compared with other approaches. Additionally, our method combining the YUV-GAN with a transfer-learning-based training strategy led to better performance in the case of scarce training data.

Cloud Removal From Optical Satellite Imagery With SAR Imagery Using Sparse Representation

2015 ◽  
Vol 12 (5) ◽  
pp. 1046-1050 ◽  
Author(s):  
Bo Huang ◽  
Ying Li ◽  
Xiaoyu Han ◽  
Yuanzheng Cui ◽  
Wenbo Li ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Satellite Imagery ◽  
Sar Imagery ◽  
Cloud Removal

scholarly journals Cloud Removal in High Resolution Multispectral Satellite Imagery: Comparing Three Approaches

Proceedings ◽  
2018 ◽  
Vol 2 (7) ◽  
pp. 353
Author(s):  
Fathalrahman Adam ◽  
Milena Mönks ◽  
Thomas Esch ◽  
Mihai Datcu
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Multispectral Satellite Imagery ◽  
Cloud Removal
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