@inproceedings{57a58d78ecab40eb829764d598306eb1,
title = "HED-UNET: A MULTI-SCALE FRAMEWORK FOR SIMULTANEOUS SEGMENTATION AND EDGE DETECTION",
abstract = "Segmentation models for remote sensing imagery are usually trained on the segmentation task alone. However, for many applications, the class boundaries carry semantic value. To account for this, we propose a new approach that unites both tasks within a single deep learning model. The proposed network architecture follows the successful encoder-decoder approach, and is improved by employing deep supervision at multiple resolution levels, as well as merging these resolution levels into a final prediction using a hierarchical attention mechanism. This framework is trained to detect the coastline in Sentinel-1 images of the Antarctic coastline. Its performance is then compared to conventional single-task approaches, and shown to outperform these methods. The code is available at https://github.com/khdlr/HED-UNet.",
keywords = "Antarctica, Edge detection, Glacier front, Semantic segmentation",
author = "Konrad Heidler and Lichao Mou and Celia Baumhoer and Andreas Dietz and Zhu, {Xiao Xiang}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 ; Conference date: 12-07-2021 Through 16-07-2021",
year = "2021",
doi = "10.1109/IGARSS47720.2021.9553585",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3037--3040",
booktitle = "IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings",
}