Synthetic aperture radar data analysis by deep learning for automatic sea ice classification

Konferenz: EUSAR 2021 - 13th European Conference on Synthetic Aperture Radar
29.03.2021 - 01.04.2021 in online

Tagungsband: EUSAR 2021

Seiten: 6Sprache: EnglischTyp: PDF

Khaleghian, Salman; Kramer, Thomas; Eltoft, Torbjorn; Marinoni, Andrea (Department of Science and Technology, UiT the Arctic University of Norway, Tromsø, Norway)
Everett, Alistair; Kiarbech, Ashild; Hughes, Nick (Norwegian Sea Ice Service, Norwegian Meteorological Institute, Langnes, Tromsø, Norway)

In this paper, we explore the potential of deep learning (DL) networks to produce reliable sea ice classification by analyzing data collected by synthetic aperture radar (SAR) sensors over polar regions. Taking advantage of their ability to extract features from complex datasets, DL schemes can be used to perform large scale data investigation, so to help manual interpretation conducted by experts in sea ice charting services. We highlighted the ability of different DL settings as well as their limits (mainly associated with scarce training data). Experimental results show the validation accuracy and the inference potential of three DL networks.