scholarly journals Using Web images to train a deep neural network to detect sparsely distributed wildlife in large volumes of remotely sensed imagery: A case study of polar bears on sea ice

2022 ◽  
pp. 101547
Author(s):  
Dominique Chabot ◽  
Seth Stapleton ◽  
Charles M. Francis
Keyword(s):  
Neural Network ◽  
Sea Ice ◽  
Deep Neural Network ◽  
Remotely Sensed ◽  
Polar Bears ◽  
Remotely Sensed Imagery ◽  
Web Images

scholarly journals Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

2019 ◽  
Vol 11 (23) ◽  
pp. 2864 ◽  
Author(s):  
Jiping Liu ◽  
Yuanyuan Zhang ◽  
Xiao Cheng ◽  
Yongyun Hu
Keyword(s):  
Neural Network ◽  
Sea Ice ◽  
Snow Depth ◽  
Deep Neural Network ◽  
Arctic Basin ◽  
Temporal Variations ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Thickness ◽  
Arctic Sea

The accurate knowledge of spatial and temporal variations of snow depth over sea ice in the Arctic basin is important for understanding the Arctic energy budget and retrieving sea ice thickness from satellite altimetry. In this study, we develop and validate a new method for retrieving snow depth over Arctic sea ice from brightness temperatures at different frequencies measured by passive microwave radiometers. We construct an ensemble-based deep neural network and use snow depth measured by sea ice mass balance buoys to train the network. First, the accuracy of the retrieved snow depth is validated with observations. The results show the derived snow depth is in good agreement with the observations, in terms of correlation, bias, root mean square error, and probability distribution. Our ensemble-based deep neural network can be used to extend the snow depth retrieval from first-year sea ice (FYI) to multi-year sea ice (MYI), as well as during the melting period. Second, the consistency and discrepancy of snow depth in the Arctic basin between our retrieval using the ensemble-based deep neural network and two other available retrievals using the empirical regression are examined. The results suggest that our snow depth retrieval outperforms these data sets.

Sea Ice Classification via Deep Neural Network Semantic Segmentation

2020 ◽  
pp. 1-1
Author(s):  
Benjamin Dowden ◽  
Oscar De Silva ◽  
Weimin Huang ◽  
Dan Oldford
Keyword(s):  
Neural Network ◽  
Sea Ice ◽  
Deep Neural Network ◽  
Semantic Segmentation

Training strategies for neural network soft classification of remotely-sensed imagery

1997 ◽  
Vol 18 (8) ◽  
pp. 1851-1856 ◽  
Author(s):  
A. C. Bernard ◽  
G. G. Wilkinson ◽  
I. Kanellopoulos
Keyword(s):  
Neural Network ◽  
Remotely Sensed ◽  
Remotely Sensed Imagery ◽  
Soft Classification ◽  
Training Strategies
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