RADARSAT-2 Synthetic-Aperture Radar Land Cover Segmentation Using Deep Convolutional Neural Networks

2021 ◽  
pp. 106-117
Author(s):  
Mirmohammad Saadati ◽  
Marco Pedersoli ◽  
Patrick Cardinal ◽  
Peter Oliver
Keyword(s):  
Neural Networks ◽  
Land Cover ◽  
Synthetic Aperture Radar ◽  
Convolutional Neural Networks ◽  
Synthetic Aperture ◽  
Deep Convolutional Neural Networks ◽  
Aperture Radar

scholarly journals Iceberg Detection using Satellite Images

2021 ◽  
Vol 9 (VII) ◽  
pp. 1896-1901
Author(s):  
Dr. Ashoka K
Keyword(s):  
Neural Networks ◽  
Synthetic Aperture Radar ◽  
Convolutional Neural Networks ◽  
Satellite Images ◽  
Ice Sheet ◽  
Synthetic Aperture ◽  
The Arctic ◽  
Ice Shelves ◽  
Ice Conditions ◽  
Aperture Radar

Chunks of ice present genuine risks for transport route and seaward establishments. Subsequently, there is a huge interest to limit them ideal and over tremendous regions. As a result of their autonomy of overcast cover and sunlight, satellite Synthetic Aperture Radar (SAR) pictures are among the favoured information hotspots for functional ice conditions and ice sheet events. The picture spatial goal for the most part utilized for chunk of ice observing changes between a couple and 100 m. Prepared SAR information are portrayed by dot clamour, which causes a grainy appearance of the pictures making the distinguishing proof of ice shelves amazingly troublesome. The techniques for satellite checking of hazardous ice developments, similar to ice shelves in the Arctic oceans address a danger to the security of route and monetary action on the Arctic rack. Along these lines, here we have thought of a thought of an application which distinguishes the Iceberg pictures utilizing satellite pictures and it is proposed by utilizing Convolutional Neural Networks (CNN) grouping.

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