Summary of Sea Ice Detection Using Concurrent Multispectral and Synthetic Aperture Radar Imagery, by Martin S J Rogers et al.
Sea ice detection using concurrent multispectral and synthetic aperture radar imagery
by Martin S J Rogers, Maria Fox, Andrew Fleming, Louisa van Zeeland, Jeremy Wilkinson, J. Scott Hosking
First submitted to arxiv on: 11 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel tool called ViSual_IceD, which uses convolutional neural networks (CNNs) to automatically detect sea ice using concurrent multispectral visible and Synthetic Aperture Radar (SAR) imagery. The approach combines the strengths of both MSI and SAR data, enabling the fusion and concatenation of images with different spatial resolutions. Compared to U-Net models trained on concatenated MSI and SAR imagery or exclusively on one type of data, ViSual_IceD outperforms other networks with a higher F1 score. This tool can be used in conjunction with passive microwave (PMW) sensor data, particularly in coastal regions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ViSual_IceD is a new way to detect sea ice using special cameras that take pictures from space and on the ground. Right now, we use these cameras to see how much sea ice there is, but it’s not always easy because of clouds and shadows. This tool can help us by combining what we get from the different cameras in a better way. It’s like having a superpower to see through clouds and darkness! |
Keywords
* Artificial intelligence * F1 score
