Label-free ice floes segmentation in SAR images for floe size distribution in the Antarctic

Sea ice is a crucial component of the polar marine environment. A contiguous piece of sea ice is called an ice floe, and the size variation in these floes across a region is described as the floe size distribution (FSD). Analysis of FSD provides information on the physical processes associated with sea ice dynamics, which is needed for calibrating and validating numerical sea ice models. For example, the size and shape of sea ice floes is predominantly controlled by wind and ocean wave conditions, thus the FSD metric provides crucial insight into these environmental conditions. Consequently, the automatic detection of floes, and hence FSD, is required to improve our understanding of these conditions over large spatial-temporal scales. Here, we present a method to automatically segment sea ice from Synthetic Aperture Radar (SAR) images for downstream applications. Our method uses an autoencoder architecture, minimising dual losses concurrently to guide the training on a large number of SAR images.

For machine learning (ML) to assist in automatic labelling of sea ice, traditional supervised learning models require the provision of a sufficiently large, labelled dataset to train the model. Manual interpretation and identification of sea ice in satellite imagery is a time consuming and tedious process, frustrating the development of annotations over large spatial areas. Additionally, manually labelled data are subject to unintentional human variability thus potentially introducing bias. It is not a scalable solution.

Feature learning or representation learning is a ML technique that automatically guides its own training to extract useful information without the need for labelled data. Instead of using optical images as many other works done on FSD with supervised learning techniques, we use SAR images here with representation learning. Using SAR images allows us to monitor sea ice conditions year-round, including during periods of polar darkness and cloudy conditions, where the detection of sea ice conditions in optical images is problematic. As this autoencoder model does not require labelled data, it can be scaled both spatially and temporally. It also has the potential to be extended to detect other features and to learn beyond ice-water segmentation.