Physics-Aware Training Data to Improve Machine Learning for Sea Ice Classification from Sentinel-1 SAR Scenes
Qiang Wang, Anthony P. Doulgeris, Torbjørn Eltoft
Abstract
Sea ice mapping in polar region is crucial for understanding the global climate change, benefiting disaster control for local community as well as providing accurate navigation for mariners. Currently, meteorological ice services in many countries manually create ice-charts, which is time-consuming work from domain experts. Machine learning and deep learning methodology can be utilized to automate ice charting, however, large amounts of reliable training data is crucial for implementing the technology successfully, and training data itself is sparse and costly to obtain in the Arctic areas. To obtain more training data, we proposed a two-stage methodology which incorporate the physics into training dataset generation procedure. Firstly, a physics-based incidence angle aware algorithm was employed for generating better connected and fewer reference classes to be manually labelled for training. Secondly, we enrich the training data to a balanced training set by using the physical knowledge regarding incidence angle dependence of SAR intensity. This many-fold enriched dataset can then be used for pixel-wise sea ice versus water classification by using a reduced UNet architecture. The primary result shows the sea-ice and water edges can be reasonably delineated by utilizing the physical based training dataset generation together with the reduced UNet architecture.