Calving Fronts and How to Segment Them Using Diffusion Networks

Type: MA thesis

Status: running

Date: May 1, 2024 - November 1, 2024

Supervisors: Nora Gourmelon, Marcel Dreier, Mathias Oettl, Vincent Christlein, Andreas Maier

Global warming is impacting every part of our planet, and is also responsible for the rise of sea levels
around the world, posing a threat to a majority of the world’s population living in coastal areas. While
there are multiple factors contributing to sea level rise (SLR), such as thermal expansion due to warmer
oceans, it is also in greater part caused by the melting of glaciers and ice regions which stream into
the ocean [1]. It is therefore important for us to understand and monitor glacier ice loss, specifically
for marine- or lake-terminating glaciers. We can do so by looking at calving front movement, where
calving fronts represent the border between an ocean and a glacier. Delineating this exact front position
is fundamental for analysing the health of our glaciers and how global warming is impacting them.
Manually delineating calving fronts is incredibly time intensive, which is why in recent years, researchers
have started automating this process by turning towards deep learning algorithms. Gourmelon et
al. [2] used a U-Net for segmenting SAR images into different regions and then extracted the calving
front in a post-processing step. Wu et al. [3] combined two U-Nets to develop a cross-resolution
segmentation method, which improves the network’s ability to classify the calving front by having
coarse and fine-grained feature maps interact with each other through an attention-based hooking
Diffusion models have made headlines over the past year for their ability to produce fantastically
realistic images [4]. Since the inception of diffusion models, researchers have also started using them
for image segmentation, like in SegDiff [5], which has been further explored in the medical field
with EnsemDiff [6], as well as MedSegDiff and MedSegDiff-V2 [7, 8]. In the field of calving front
delineation however, using diffusion models has not yet been tested, which is what the focus of this
thesis will be.

In detail, the thesis consists of the following parts:
• a literature review of diffusion models being used for image segmentation tasks,
• a review of diffusion models to segment SAR calving front images into different zones,
• using a diffusion model to directly segment calving front positions,
• comparing the created diffusion model against other methods that were evaluated on the CaFFe
dataset [9].


[1] Hans-Otto P¨ortner, Debra C Roberts, Val´erie Masson-Delmotte, Panmao Zhai, Melinda Tignor, Elvira
Poloczanska, and NM Weyer. The ocean and cryosphere in a changing climate. IPCC special report on the
ocean and cryosphere in a changing climate, 1155, 2019.
[2] Nora Gourmelon, Thorsten Seehaus, Matthias Braun, Andreas Maier, and Vincent Christlein. Calving
fronts and where to find them: a benchmark dataset and methodology for automatic glacier calving front
extraction from synthetic aperture radar imagery. Earth System Science Data, 14(9):4287–4313, 2022.
[3] Fei Wu, Nora Gourmelon, Thorsten Seehaus, Jianlin Zhang, Matthias Braun, Andreas Maier, and Vincent
Christlein. Amd-hooknet for glacier front segmentation. IEEE Transactions on Geoscience and Remote
Sensing, 61:1–12, 2023.
[4] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural
information processing systems, 33:6840–6851, 2020.
[5] Tomer Amit, Tal Shaharbany, Eliya Nachmani, and Lior Wolf. Segdiff: Image segmentation with diffusion
probabilistic models. arXiv preprint arXiv:2112.00390, 2021.
[6] Julia Wolleb, Robin Sandk¨uhler, Florentin Bieder, Philippe Valmaggia, and Philippe C Cattin. Diffusion
models for implicit image segmentation ensembles. In International Conference on Medical Imaging with
Deep Learning, pages 1336–1348. PMLR, 2022.
[7] Junde Wu, Huihui Fang, Yu Zhang, Yehui Yang, and Yanwu Xu. Medsegdiff: Medical image segmentation
with diffusion probabilistic model. arXiv preprint arXiv:2211.00611, 2022.
[8] Junde Wu, Rao Fu, Huihui Fang, Yu Zhang, and Yanwu Xu. Medsegdiff-v2: Diffusion based medical
image segmentation with transformer. arXiv preprint arXiv:2301.11798, 2023.
[9] Nora Gourmelon, Thorsten Seehaus, Julian Klink, Matthias Braun, Andreas Maier, and Vincent Christlein.
Caffe-a benchmark dataset for glacier calving front extraction from synthetic aperture radar imagery. In
IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium, pages 896–898. IEEE,
[10] Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming
Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in PyTorch. In NIPS
Autodiff Workshop, 2017.