Bayesian U-Net for Segmenting Glaciers in Sar Imagery

Hartmann A, Davari A, Seehaus T, Braun M, Maier A, Christlein V (2021)

Publication Type: Conference contribution, Original article

Publication year: 2021

Original Authors: Andreas Hartmann, Amirabbas Davari, Thorsten Seehaus, Matthias Braun, Andreas Maier, Vincent Christlein

Event location: Brussels BE


DOI: 10.1109/IGARSS47720.2021.9554292


Fluctuations of the glacier calving front have an important influence over the ice flow of whole glacier systems. It is therefore important to precisely monitor the position of the calving front. However, the manual delineation of SAR images is a difficult, laborious and subjective task. Convolutional neural networks have previously shown promising results in automating the glacier segmentation in SAR images, making them desirable for further exploration of their possibilities. In this work, we propose to compute uncertainty and use it in an Uncertainty Optimization regime as a novel two-stage process. By using dropout as a random sampling layer in a U-Net architecture, we create a probabilistic Bayesian Neural Network. With several forward passes we create a sampling distribution, which can estimate the model uncertainty for each pixel in the segmentation mask. The additional uncertainty map information can serve as a guideline for the experts in the manual annotation of the data. Furthermore, feeding the uncertainty map to the network leads to 95.24 % Dice similarity, which is an overall improvement in the segmentation performance compared to the state-of-the-art deterministic U-Net-based glacier segmentation pipelines.

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Hartmann, A., Davari, A., Seehaus, T., Braun, M., Maier, A., & Christlein, V. (2021). Bayesian U-Net for Segmenting Glaciers in Sar Imagery. In Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. Brussels, BE.


Hartmann, Andreas, et al. "Bayesian U-Net for Segmenting Glaciers in Sar Imagery." Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels 2021.

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