HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline

Heidler, Konrad; Mou, Lichao; Baumhoer, Celia; Dietz, A.J.; Zhu, Xiao Xiang

doi:10.1109/tgrs.2021.3064606

Cited by 108 publications

(74 citation statements)

References 66 publications

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“…The channel attention module outputs a tensor of shape 1 × 1 × C, where C represents the number of channels, while the spatial attention module outputs a 2D attention map of the shape of the input. HED-UNet [8] uses an encoder-decoder network similar to that employed by UNet. Their main contribution is combining the coastline's segmentation and edge detection.…”

Section: Literature Reviewmentioning

confidence: 99%

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Semantic Segmentation and Edge Detection—Approach to Road Detection in Very High Resolution Satellite Images

Ghandorh

Boulila

Masood³

et al. 2022

Remote Sensing

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Road detection technology plays an essential role in a variety of applications, such as urban planning, map updating, traffic monitoring and automatic vehicle navigation. Recently, there has been much development in detecting roads in high-resolution (HR) satellite images based on semantic segmentation. However, the objects being segmented in such images are of small size, and not all the information in the images is equally important when making a decision. This paper proposes a novel approach to road detection based on semantic segmentation and edge detection. Our approach aims to combine these two techniques to improve road detection, and it produces sharp-pixel segmentation maps, using the segmented masks to generate road edges. In addition, some well-known architectures, such as SegNet, used multi-scale features without refinement; thus, using attention blocks in the encoder to predict fine segmentation masks resulted in finer edges. A combination of weighted cross-entropy loss and the focal Tversky loss as the loss function is also used to deal with the highly imbalanced dataset. We conducted various experiments on two datasets describing real-world datasets covering the three largest regions in Saudi Arabia and Massachusetts. The results demonstrated that the proposed method of encoding HR feature maps effectively predicts sharp segmentation masks to facilitate accurate edge detection, even against a harsh and complicated background.

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Section: Literature Reviewmentioning

confidence: 99%

“…Not all of the pixels in a feature map need equal attention; therefore, Ref. [8] used hierarchical attention maps to enable the merging of predictions from multiple scales, allowing the network to focus on the features that it thinks are more important from each scale.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation and Edge Detection—Approach to Road Detection in Very High Resolution Satellite Images

Ghandorh

Boulila

Masood³

et al. 2022

Remote Sensing

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“…By combining the HED and U‐Net, Heidler et al. (2021) recently successfully extracted the Antarctic coast's waterline and completed the sea‐land classification simultaneously for Sentinel‐1 SAR imagery. These studies show that the DCNN‐based methods have great potential and application value in the automatic extraction of waterlines in high‐spatial‐resolution SAR images.…”

Section: Introductionmentioning

confidence: 99%

“…Baumhoer et al (2019) developed a U-Net model to extract the Antarctic coastline for tracking glacier and ice shelf front movement from European Space Agency's Sentinel-1 data. By combining the HED and U-Net, Heidler et al (2021) recently successfully extracted the Antarctic coast's waterline and completed the sea-land classification simultaneously for Sentinel-1 SAR imagery. These studies show that the DCNN-based methods have great potential and application value in the automatic extraction of waterlines in high-spatial-resolution SAR images.…”

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confidence: 99%

Automatic Waterline Extraction and Topographic Mapping of Tidal Flats From SAR Images Based on Deep Learning

Zhang

Wang

et al. 2022

Geophysical Research Letters

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This study presented an intuitive approach to derive large‐scale tidal flat's Digital Elevation Model (DEM). We first developed an automated method for accurately extracting the waterline from Synthetic Aperture Radar images acquired in Subei Sandbanks along the Yellow Sea coast of China between 2015 and 2020 based on deep convolutional neural networks. The statistical results show this method has appreciable accuracy for efficient waterline extraction even under complex imaging conditions with a mean recall and precision of 0.90 and 0.80, respectively. Then the pixel‐level extracted waterlines are calibrated with a global tide model to construct the large‐scale tidal flat's DEM in the study region. The comparison against in situ topographic data shows an error of 29 cm, demonstrating the usefulness of monitoring the morpho‐sedimentary evolution in intertidal areas. Furthermore, the Subei Sandbanks remained stable from 2015 to 2020, while the coastal region changed drastically due to human activities.

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“…Making this observation, it is only natural to ask the question "can we exploit the relationship between segmentation and edge detection?" An extended version of this conference report is available at [1]. Given the fact that semantic segmentation models often produce blurry results close to the class borders (cf.…”

Section: Introductionmentioning

confidence: 99%

Hed-Unet: A Multi-Scale Framework for Simultaneous Segmentation and Edge Detection

Heidler

Mou

Baumhoer

et al. 2021

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

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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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HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline

Cited by 108 publications

References 66 publications

Semantic Segmentation and Edge Detection—Approach to Road Detection in Very High Resolution Satellite Images

Semantic Segmentation and Edge Detection—Approach to Road Detection in Very High Resolution Satellite Images

Automatic Waterline Extraction and Topographic Mapping of Tidal Flats From SAR Images Based on Deep Learning

Hed-Unet: A Multi-Scale Framework for Simultaneous Segmentation and Edge Detection

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