We propose an algorithm for ice crack identification and detection using u-net network, which can realize the automatic detection of Antarctic ice cracks. Based on the data of sentinel-1 EW from January to February every year, in order to suppress the speckle noise of SAR image, the probabilistic patch based weights (ppb) algorithm is selected for filtering, and then representative samples are selected and input into the u-net network for model training, and the ice cracks are predicted according to the trained model. Taking five typical ice shelves（Amery、Fimbul、Nickerson、Shackleton、Thwaiters) in Antarctica as an example, the average accuracy of classification results can reach 94.5%, of which the local accuracy of fissure area can reach 78.6%, and the recall rate is 89.4%.

File naming and required software

Crevasses data is stored in shpfile format, and the file is named ice shelf name + year.Shp. All shpfile files in the dataset can be opened directly with arcgis/qgis and other software.

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Cite as:

Li, X., Liang, S., Yang, B., Zhao, J. (2022). Crevasse dataset over typical ice shelves in Antarctica（2015、2016、2020）. A Big Earth Data Platform for Three Poles, DOI: 10.11888/Cryos.tpdc.272745. CSTR: 18406.11.Cryos.tpdc.272745. (Download the reference： RIS | Bibtex )

Related Literatures:

1. Zhao, J., Liang, S., & Li, X., et al. (2022). Detection of Surface Crevasses over Antarctic Ice Shelves Using SAR Imagery and Deep Learning Method. Remote Sensing, 14(3), 487.( View Details | Bibtex)

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Support Program

CASEarth:Big Earth Data for Three Poles（grant No. XDA19070000） (No:XDA19000000)

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License： This work is licensed under an Attribution 4.0 International (CC BY 4.0)