  Knowledge on changes in glacier calving front positions is important for
assessing the status of glaciers. Remote sensing imagery provides the ideal
database for monitoring calving front positions, however, it is not feasible to
perform this task manually for all calving glaciers globally due to
time-constraints. Deep learning-based methods have shown great potential for
glacier calving front delineation from optical and radar satellite imagery. The
calving front is represented as a single thin line between the ocean and the
glacier, which makes the task vulnerable to inaccurate predictions. The limited
availability of annotated glacier imagery leads to a lack of data diversity
(not all possible combinations of different weather conditions, terminus
shapes, sensors, etc. are present in the data), which exacerbates the
difficulty of accurate segmentation. In this paper, we propose
Attention-Multi-hooking-Deep-supervision HookNet (AMD-HookNet), a novel glacier
calving front segmentation framework for synthetic aperture radar (SAR) images.
The proposed method aims to enhance the feature representation capability
through multiple information interactions between low-resolution and
high-resolution inputs based on a two-branch U-Net. The attention mechanism,
integrated into the two branch U-Net, aims to interact between the
corresponding coarse and fine-grained feature maps. This allows the network to
automatically adjust feature relationships, resulting in accurate
pixel-classification predictions. Extensive experiments and comparisons on the
challenging glacier segmentation benchmark dataset CaFFe show that our
AMD-HookNet achieves a mean distance error of 438 m to the ground truth
outperforming the current state of the art by 42%, which validates its
effectiveness.
