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Part 1: Document Description
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Citation |
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Title: |
Synthetic-Aperture Radar (SAR) based Ice types/Ice edge dataset for deep learning analysis |
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Identification Number: |
doi:10.18710/QAYI4O |
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Distributor: |
DataverseNO |
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Date of Distribution: |
2020-07-23 |
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Version: |
1 |
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Bibliographic Citation: |
Khaleghian, Salman; Lohse, Johannes Philipp; Kræmer, Thomas, 2020, "Synthetic-Aperture Radar (SAR) based Ice types/Ice edge dataset for deep learning analysis", https://doi.org/10.18710/QAYI4O, DataverseNO, V1 |
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Citation |
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Title: |
Synthetic-Aperture Radar (SAR) based Ice types/Ice edge dataset for deep learning analysis |
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Identification Number: |
doi:10.18710/QAYI4O |
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Authoring Entity: |
Khaleghian, Salman (UiT The Arctic University of Norway) |
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Lohse, Johannes Philipp (UiT The Arctic University of Norway) |
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Kræmer, Thomas (UiT The Arctic University of Norway) |
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Producer: |
UiT The Arctic University of Norway |
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Distributor: |
DataverseNO |
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Distributor: |
UiT The Arctic University of Norway |
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Access Authority: |
Khaleghian, Salman |
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Depositor: |
Khaleghian, Salman |
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Date of Deposit: |
2019-12-18 |
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Holdings Information: |
https://doi.org/10.18710/QAYI4O |
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Study Scope |
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Keywords: |
Computer and Information Science, Earth and Environmental Sciences, Engineering, Physics, Deep Learning, Remote Sensing, Ice types, Ice Edge, Synthetic-aperture radar |
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Abstract: |
This dataset has been prepared for Ice types/Ice edge analysis based on deep neural networks. The dataset has been created based on 31 scenes in north of Svalbard based on labeled polygons. The dataset contains six classes including OpenWater, Leads with water, Brash/Pancake Ice, Thin Ice, Thick Ice-Flat and Thick Ice-Ridged. The data records, called patches, extracted all from inside of each polygon with stride 10 in different sizes, 10x10, 20x20, 32x32, 36x36, 46x46 pixels for each class |
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Date of Collection: |
2018-01-01-2018-12-31 |
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Kind of Data: |
SAR Image |
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Kind of Data: |
Satellite image |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
The file not available now, you should contact the author to get access. |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
Khaleghian, S.; Ullah, H.; Kræmer, T.; Hughes, N.; Eltoft, T.; Marinoni, A. Sea Ice Classification of SAR Imagery Based on Convolution Neural Networks. Remote Sens. 2021, 13, 1734. https://doi.org/10.3390/rs13091734 |
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Identification Number: |
10.3390/rs13091734 |
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Bibliographic Citation: |
Khaleghian, S.; Ullah, H.; Kræmer, T.; Hughes, N.; Eltoft, T.; Marinoni, A. Sea Ice Classification of SAR Imagery Based on Convolution Neural Networks. Remote Sens. 2021, 13, 1734. https://doi.org/10.3390/rs13091734 |
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Label: |
00_Readme.txt |
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Notes: |
text/plain |
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Label: |
Patches10x10.zip |
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Notes: |
application/zip |
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Label: |
Patches20x20.zip |
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Notes: |
application/zip |
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Label: |
Patches32x32.zip |
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Notes: |
application/zip |
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Label: |
patches36x36.zip |
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Notes: |
application/zip |
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Label: |
Patches46x46.zip |
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Notes: |
application/zip |