Summary of Interpreting a Semantic Segmentation Model For Coastline Detection, by Conor O’sullivan et al.
Interpreting a Semantic Segmentation Model for Coastline Detection
by Conor O’Sullivan, Seamus Coveney, Xavier Monteys, Soumyabrata Dev
First submitted to arxiv on: 19 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 research paper investigates a deep-learning semantic segmentation model used for classifying coastline satellite images into land and water. The goal is to build trust in the model, gain insights into coastal water body extraction, and identify the most important spectral bands for predicting segmentation masks. The study employs a permutation importance approach to achieve this. Results indicate that Near-Infrared (NIR) is the most crucial band, with a significant accuracy drop of 38.12 percentage points when permuted. Other key bands include Water Vapour, SWIR 1, and Blue, while Coastal Aerosol, Green, Red, RE1-RE4, and SWIR 2 are deemed non-essential and can be omitted from future model builds to simplify complexity and reduce computational requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses a special kind of computer vision called deep learning to better understand how to identify land and water on satellite images. The researchers want to make sure their approach is trustworthy and learn which parts of the image are most important for this task. They used a clever method to figure out which colors or “bands” in the image matter most, and found that some bands are much more important than others. In fact, one band called Near-Infrared is super important! This discovery could help make their computer program better at identifying land and water on satellite images. |
Keywords
» Artificial intelligence » Deep learning » Semantic segmentation
