Glacier Calving Front Segmentation Using Attention U-Net

TL;DR

This paper introduces an Attention U-Net model for glacier calving front segmentation from SAR images, enhancing interpretability and achieving slightly better accuracy than standard U-Net.

Contribution

The study demonstrates the integration of attention mechanisms into U-Net for glacier front segmentation, providing insights into the model's focus regions and improving prediction accuracy.

Findings

Attention U-Net performs comparably to standard U-Net.

Achieves up to 1.5% higher Dice score.

Provides attention maps for interpretability.

Abstract

An essential climate variable to determine the tidewater glacier status is the location of the calving front position and the separation of seasonal variability from long-term trends. Previous studies have proposed deep learning-based methods to semi-automatically delineate the calving fronts of tidewater glaciers. They used U-Net to segment the ice and non-ice regions and extracted the calving fronts in a post-processing step. In this work, we show a method to segment the glacier calving fronts from SAR images in an end-to-end fashion using Attention U-Net. The main objective is to investigate the attention mechanism in this application. Adding attention modules to the state-of-the-art U-Net network lets us analyze the learning process by extracting its attention maps. We use these maps as a tool to search for proper hyperparameters and loss functions in order to generate higher qualitative results. Our proposed attention U-Net performs comparably to the standard U-Net while providing additional insight into those regions on which the network learned to focus more. In the best case, the attention U-Net achieves a 1.5% better Dice score compared to the canonical U-Net with a glacier front line prediction certainty of up to 237.12 meters.