Dissecting Glaciers - Can an Automated Bio-Medical Image Segmentation Tool also Segment Glaciers?

The temporal variability of glacier calving front positions provides essential information about the state of marine-terminating glaciers. These positions can be extracted from Synthetic Aperture Radar (SAR) images throughout the year. To automate this extraction, we apply deep learning techniques that segment the SAR images into different classes: glacier; ocean including ice-melange and sea-ice covered ocean; rock outcrop; and regions with no information like areas outside the SAR swath, layover regions and SAR shadow. The calving front position can be derived from these regions during post-processing.   
A downside of deep learning is that hyper-parameters need to be tuned manually. For this tuning, expert knowledge and experience in deep learning are required. Furthermore, the fine-tuning process takes up much time, and the researcher needs to have programming skills.
    
In the biomedical imaging domain, a deep learning framework [1] has become increasingly popular for image segmentation. The nnU-Net can be used out-of-the-box. It automatically adapts the U-Net, the state-of-the-art architecture for image segmentation, to different datasets and segmentation tasks. Hence, no more manual tuning is required. The framework outperforms specialized deep learning pipelines in a multitude of public biomedical segmentation competitions.   
We apply the nnU-Net to the task of glacier segmentation, investigating whether the framework is also beneficial in the domain of remote sensing. Therefore, we train and test the nnU-Net on CaFFe (https://github.com/Nora-Go/CaFFe), a benchmark dataset for automatic calving front detection on SAR images. CaFFe comprises geocoded, orthorectified imagery acquired by the satellite missions RADARSAT-1, ERS-1/2, ALOS PALSAR, TerraSAR-X, TanDEM-X, Envisat, and Sentinel-1, covering the period 1995 - 2020. The ground range resolution varies between 7 and 20 m². The nnU-Net learns from the multi-class "zones" labels provided with the dataset. We adopt the post-processing scheme from Gourmelon et al. [2] to extract the front from the segmented landscape regions. The test set includes images from the Mapple Glacier located on the Antarctic Peninsula and the Columbia Glacier in Alaska. The nnU-Net's calving front predictions for the Mapple Glacier lie close to the ground truth with just 125 m mean distance error. As the Columbia Glacier shows several calving front sections, its segmentation is more difficult than that of the laterally constrained Mapple Glacier. This complexity of the calving fronts is also reflected in the results: Predictions for the Columbia Glacier show a mean distance error of 635 m. Concludingly, the results demonstrate that the nnU-Net holds considerable potential for the remote sensing domain, especially for glacier segmentation.
    
[1] Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18, 203–211 (2021). https://doi.org/10.1038/s41592-020-01008-z 

[2] Gourmelon, N., Seehaus, T., Braun, M., Maier, A., Christlein, V.: Calving Fronts and Where to Find Them: A Benchmark Dataset and Methodology for Automatic Glacier Calving Front Extraction from SAR Imagery, In Prep.