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...
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ftzenodo:oai:zenodo.org:5075448 2024-09-15T18:09:55+00:00 Semantic Sea-Ice Classification for Belgica Bank in Greenland Dumitru Octavian Karmakar Chandrabali Datcu Mihai 2021-07-06 https://doi.org/10.5281/zenodo.5075448 unknown Zenodo https://igarss2021.com/view_paper.php?PaperNum=3147 https://doi.org/10.1109/JSTARS.2020.3039012 https://doi.org/10.5194/isprs-archives-XLII-2-W16-83-2019 https://zenodo.org/communities/polarops https://zenodo.org/communities/eu https://doi.org/10.5281/zenodo.5075447 https://doi.org/10.5281/zenodo.5075448 oai:zenodo.org:5075448 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Semantic labelling sea-ice classes active learning info:eu-repo/semantics/other 2021 ftzenodo https://doi.org/10.5281/zenodo.507544810.1109/JSTARS.2020.303901210.5194/isprs-archives-XLII-2-W16-83-201910.5281/zenodo.5075447 2024-07-26T06:13:33Z 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. Other/Unknown Material Greenland Sea ice Zenodo |
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Semantic labelling sea-ice classes active learning |
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Semantic labelling sea-ice classes active learning Dumitru Octavian Karmakar Chandrabali Datcu Mihai Semantic Sea-Ice Classification for Belgica Bank in Greenland |
topic_facet |
Semantic labelling sea-ice classes active learning |
description |
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. |
format |
Other/Unknown Material |
author |
Dumitru Octavian Karmakar Chandrabali Datcu Mihai |
author_facet |
Dumitru Octavian Karmakar Chandrabali Datcu Mihai |
author_sort |
Dumitru Octavian |
title |
Semantic Sea-Ice Classification for Belgica Bank in Greenland |
title_short |
Semantic Sea-Ice Classification for Belgica Bank in Greenland |
title_full |
Semantic Sea-Ice Classification for Belgica Bank in Greenland |
title_fullStr |
Semantic Sea-Ice Classification for Belgica Bank in Greenland |
title_full_unstemmed |
Semantic Sea-Ice Classification for Belgica Bank in Greenland |
title_sort |
semantic sea-ice classification for belgica bank in greenland |
publisher |
Zenodo |
publishDate |
2021 |
url |
https://doi.org/10.5281/zenodo.5075448 |
genre |
Greenland Sea ice |
genre_facet |
Greenland Sea ice |
op_relation |
https://igarss2021.com/view_paper.php?PaperNum=3147 https://doi.org/10.1109/JSTARS.2020.3039012 https://doi.org/10.5194/isprs-archives-XLII-2-W16-83-2019 https://zenodo.org/communities/polarops https://zenodo.org/communities/eu https://doi.org/10.5281/zenodo.5075447 https://doi.org/10.5281/zenodo.5075448 oai:zenodo.org:5075448 |
op_rights |
info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode |
op_doi |
https://doi.org/10.5281/zenodo.507544810.1109/JSTARS.2020.303901210.5194/isprs-archives-XLII-2-W16-83-201910.5281/zenodo.5075447 |
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