Evaluation of a Modified U-Net with Dropout and a Multi-Task Model for Glacier Calving Front Segmentation

Evaluation of a Modified U-Net with Dropout and a Multi-Task Model for Glacier Calving Front Segmentation

With global temperatures rising, the tracking and prediction of glacier changes become more and more relevant. Part of these efforts is the development of Neural Network Algorithms to automatically detect calving fronts of marine-terminating glaciers. Gourmelon et al. [1] introduce the first publicly available benchmark dataset for calving front delineation on synthetic aperture radar (SAR) imagery dubbed CaFFe. The dataset consists of the SAR imagery and two corresponding labels: one showing the calving front vs the background and the other showing different landscape regions. Moreover, the paper provides two deep learning models as baselines, one for each label. As there are many different approaches to calving front delineation the question of what method provides the best performance arises. Subsequently, the aim of this thesis is to evaluate the codes of the following two papers [2],[3] on the CaFFe benchmark dataset and compare their performance with the baselines provided by Gourmelon et al. [1].

• paper 1:

Mohajerani et al. [2] employs a Convolutional Neural Network (CNN) with a modified U-Net architecture that also incorporates additional dropout layers. The CNN uses, in contrast to Gourmelon et al. [1], optical imagery as its input.

• paper 2:

Heidler et al. [3] introduces a deep learning model for coastline detection, which combines the two tasks of segmenting water and land and binary coastline delineation into one cohesive multi-task deep learning model.

To make a fair and reasonable comparison, the hyperparameters of each model will be optimized on the CaFFe benchmark dataset and the model weights will be re-trained on CaFFe’s train set. The evaluation will be conducted on the provided test set and the metrics introduced in Gourmelon et al. [1] will be used for the comparison.

References

[1] Gourmelon, N.; Seehaus, T.; Braun, M.; Maier, A.; and Christlein, V.: Calving Fronts and Where to Find Them: A Benchmark Dataset and Methodology for Automatic Glacier Calving Front Extraction from SAR Imagery, Earth Syst. Sci. Data Discuss. [preprint]. 2022, https://doi.org/10.5194/essd-2022-139, in review.

[2] Mohajerani, Y.; Wood, M.; Velicogna, I.; and Rignot, E.: Detection of Glacier Calving Margins with Convolutional Neural Networks: A Case Study, Remote Sens. 2019, 11, 74. https://doi.org/10.3390/rs11010074

[3] Heidler, K.; Mou, L.; Baumhoer, C.; Dietz A.; and Zhu, X.: HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline, IEEE Transactions on Geoscience and Remote Sensing. 2022, vol. 60, 1-14, Art no. 4300514, doi: 10.1109/TGRS.2021.3064606.