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

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.