Convolutional neural networks for satellite remote sensing at coarse resolution. Application for the SST retrieval using IASI

2021 ◽  
Vol 263 ◽  
pp. 112553
Author(s):  
Filipe Aires ◽  
Élodie Boucher ◽  
Victor Pellet
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Satellite Remote Sensing ◽  
Coarse Resolution ◽  
Sst Retrieval

scholarly journals SNOW AND CLOUD DISCRIMINATION USING CONVOLUTIONAL NEURAL NETWORKS

2018 ◽  
Vol IV-5 ◽  
pp. 59-63 ◽  
Author(s):  
D. Varshney ◽  
P. K. Gupta ◽  
C. Persello ◽  
B. R. Nikam
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Satellite Remote Sensing ◽  
Cloud Cover ◽  
Short Wave ◽  
High Accuracy ◽  
Traditional Methods ◽  
Cloud Mask ◽  
Climate Studies

<p><strong>Abstract.</strong> Snow is an important feature on our planet, and measuring its extent has advantages in climate studies. Snow mapping through satellite remote sensing is often affected by cloud cover. This issue can be resolved by using short wave infrared (SWIR) sensors. In order to obtain an effective cloud mask, our study aims to use SWIR data of a ResourceSat-2 satellite. We employ Convolutional Neural Networks (CNN) to discriminate similar pixels of clouds and snow. The technique is expected to give a high accuracy compared to traditional methods such as thresholding. The cloud mask thus produced can also be used for creating the metadata for Indian Remote Sensing products.</p>

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