Summary of Toward Polar Sea-ice Classification Using Color-based Segmentation and Auto-labeling Of Sentinel-2 Imagery to Train An Efficient Deep Learning Model, by Jurdana Masuma Iqrah et al.
Toward Polar Sea-Ice Classification using Color-based Segmentation and Auto-labeling of Sentinel-2 Imagery to Train an Efficient Deep Learning Model
by Jurdana Masuma Iqrah, Younghyun Koo, Wei Wang, Hongjie Xie, Sushil Prasad
First submitted to arxiv on: 8 Mar 2023
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 The paper develops a system for classifying polar sea ice using Sentinel-2 satellite imagery, focusing on distinguishing between thick or snow-covered, young or thin, or open water. The challenge lies in the lack of labeled training data. A novel method is presented to segment and auto-label S2 images based on color thresholds, which are then used to train a U-Net machine model. Evaluation results demonstrate high accuracy (90.18% and 91.39%) for classifying sea ice using this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research helps us better understand the melting of polar sea ice due to global warming. Scientists use satellite images to study this issue, but there’s a problem: they don’t have enough labeled data to train their machines. The team in this paper finds a way to automatically label these images and uses them to teach a computer model to tell apart different types of sea ice. This can help us make more accurate predictions about the impact of global warming. |
