Summary of Polmerlin: Self-supervised Polarimetric Complex Sar Image Despeckling with Masked Networks, by Shunya Kato et al.
PolMERLIN: Self-Supervised Polarimetric Complex SAR Image Despeckling with Masked Networks
by Shunya Kato, Masaki Saito, Katsuhiko Ishiguro, Sol Cummings
First submitted to arxiv on: 15 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 proposed channel masking self-supervised despeckling approach extends existing models for single-polarization SAR images to handle multi-polarization SAR images. This technique exploits the relationship between polarizations, utilizing spatial masking methods that address pixel-to-pixel correlations. As a result, the approach outperforms current state-of-the-art methods in both synthetic and real-world scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have blurry pictures taken from space using special radar technology called Synthetic Aperture Radar (SAR). To make these images clearer, scientists need to remove noise and speckles. This task is called despeckling. Normally, this requires having clean images to compare with noisy ones. But what if we only have the noisy images? That’s where deep learning comes in! Researchers have developed denoising models that can learn from just noisy images. The problem is that these models only work for single-polarization images and don’t handle multi-polarization images taken by modern satellites. This paper presents a new way to despeckle multi-polarization SAR images using a self-supervised approach called channel masking. It also uses spatial masking to improve the results. |
Keywords
* Artificial intelligence * Deep learning * Self supervised
