Semantic Sea-Ice Classification for Belgica Bank in Greenland
Each Sentinel-1 image is tiled into patches of 256x256 pixels. The size of the images is different and we reduced to the smallest one. In total for each image are 6,400 patches [1-2, 4]. See the excel file for the 24 Sentinel-1 ids. The semantic classes are: Black border Old ice First-Year ice Glaci...
| Main Authors: | , , |
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| Format: | Dataset |
| Language: | unknown |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://zenodo.org/record/5075448 https://doi.org/10.5281/zenodo.5075448 |
| Summary: | Each Sentinel-1 image is tiled into patches of 256x256 pixels. The size of the images is different and we reduced to the smallest one. In total for each image are 6,400 patches [1-2, 4]. See the excel file for the 24 Sentinel-1 ids. The semantic classes are: Black border Old ice First-Year ice Glaciers Icebergs Mountains Young ice Water group The last class combines the Floating ice, Water body, Water ice current and melted snow defined in [3] because they have very similar physical properties. References: 1. C.O. Dumitru, G. Schwarz, C. Karmakar, and M. Datcu, “Machine Learning-Based Paradigm for Boosting the Semantic Annotation of EO Images”, IGARSS, Belgium, July 2021, pp. 1-4. 2. C. Karmakar, C.O. Dumitru, and M. Datcu, “Explainable AI for SAR Image Time Series: Knowledge Extraction for Polar Areas”, MDPI Remote Sensing Journal, 2021, pp. 1-21 (under review). 3. C.O. Dumitru, V. Andrei, G. Schwarz, and M. Datcu, “Machine Learning for Sea Ice Monitoring from Satellites”, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W16, pp. 83-89, 2019. 4. C. Karmakar, C.O. Dumitru, G. Schwarz, and M. Datcu, “Feature-Free Explainable Data Mining in SAR Images Using Latent Dirichlet Allocation”, IEEE JSTARS, vol. 14, pp. 676-689, 2021. |
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