Javad Sheikh
Javad Sheikh
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Deep Learning
Sea Ice Concentration Estimation Via Fusion of Sentinel-1 and AMSR2 based on Encoder-Decoder Architecture
This paper presents a new method using a Convolutional Neural Network (CNN) to estimate sea ice concentration in the Baltic Sea by combining Sentinel-1 and AMSR2 data. The CNN architecture retains different resolution inputs without losing valuable information. By using a focal loss function and skip connection, the proposed model achieves a high R2 score of 90.6%.
Javad Sheikh
,
Fahimeh Farahnakian
,
Farshad Farahnakian
,
Jukka Heikkonen
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DOI
Ice-Water Segmentation Using Deep Convolutional Neural Network-Based Fusion Approach
Presenting a novel CNN model merging SAR and AMSR2 imaging for precise maritime ice-water detection. This fusion enables automatic sea ice chart generation, outperforming uni-modal SAR architecture with 94.60% pixel-wise accuracy and an F1-score of 94.99%.
Javad Sheikh
,
Fahimeh Farahnakian
,
Farshad Farahnakian
,
Jukka Heikkonen
Cite
DOI
Anomaly detection on time series using deep neural networks
Despite significant advances in artificial intelligence and high-speed hardware, computers still do not have the same human …
Javad Sheikh
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