Sea-ice states: unsupervised segmentation from multispectral data based on Mumford–Shah functional
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| Format: | Recurso digital |
| Langue: | anglais |
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2024
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| _version_ | 1866902259234766848 |
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| author | Cohrs, Jan-Christopher Kim, Ekaterina Berkels, Benjamin |
| author_facet | Cohrs, Jan-Christopher Kim, Ekaterina Berkels, Benjamin |
| contents | <p><strong>Abstract</strong></p> <p>We hypothesize that different states of sea ice cover will reflect a characteristic spectrum that acts like a fingerprint. Hence, accounting for spectral variability, we would have well separated spectral clusters, one for each ‘sea-ice state’ that we have in the scene. To study this, we have applied the distribution-dependent Mumford–Shah model (Cohrs et al., 2022) to segment data from the Sentinel-2 multi-spectral imaging mission while focusing on sea ice cover. This unsupervised model performs a clustering of the pixel spectra while accounting for spatial neighborhood relations of the pixels to make use of the full information. Preliminary results from the numerical experiments show that the segmentation approach enables derivation of total ice concentration and more complex sea ice states. The performance is sensitive to the<br>degree of spatial regularization. To our knowledge, this is the first study that uses all (except for B10, following Lanaras et al. (2018)) spectral bands from the Sentinel-2 multi-spectral instrument to segment sea ice cover without any labeled training data. The presented technique has potential to improve current ice concentration retrieval algorithms and can be particularly beneficial for investigating spectral characteristics of different sea-ice states.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14541868 |
| institution | Zenodo |
| language | eng |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Sea-ice states: unsupervised segmentation from multispectral data based on Mumford–Shah functional Cohrs, Jan-Christopher Kim, Ekaterina Berkels, Benjamin fingerprint spectra Mumford–Shah model sea ice segmentation Arctic sea ice loss <p><strong>Abstract</strong></p> <p>We hypothesize that different states of sea ice cover will reflect a characteristic spectrum that acts like a fingerprint. Hence, accounting for spectral variability, we would have well separated spectral clusters, one for each ‘sea-ice state’ that we have in the scene. To study this, we have applied the distribution-dependent Mumford–Shah model (Cohrs et al., 2022) to segment data from the Sentinel-2 multi-spectral imaging mission while focusing on sea ice cover. This unsupervised model performs a clustering of the pixel spectra while accounting for spatial neighborhood relations of the pixels to make use of the full information. Preliminary results from the numerical experiments show that the segmentation approach enables derivation of total ice concentration and more complex sea ice states. The performance is sensitive to the<br>degree of spatial regularization. To our knowledge, this is the first study that uses all (except for B10, following Lanaras et al. (2018)) spectral bands from the Sentinel-2 multi-spectral instrument to segment sea ice cover without any labeled training data. The presented technique has potential to improve current ice concentration retrieval algorithms and can be particularly beneficial for investigating spectral characteristics of different sea-ice states.</p> |
| title | Sea-ice states: unsupervised segmentation from multispectral data based on Mumford–Shah functional |
| topic | fingerprint spectra Mumford–Shah model sea ice segmentation Arctic sea ice loss |
| url | https://doi.org/10.5281/zenodo.14541868 |