HED-UNet: A multi-scale framework for simultaneous segmentation and edge detection
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 networ...
| Published in: | 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS |
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| Main Authors: | , , , , |
| Format: | Conference Object |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://elib.dlr.de/143088/ https://elib.dlr.de/143088/1/hed_unet-igarss.pdf |
| Summary: | 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 |
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