Road Extraction Method of Remote Sensing Image Based on Deformable Attention Transformer Article Swipe
Ling Zhao
,
Jianing Zhang
,
Xiujun Meng
,
Wenming Zhou
,
Zhenshi Zhang
,
Chengli Peng
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/sym16040468
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/sym16040468
Road extraction is a typical task in the semantic segmentation of remote sensing images, and one of the most efficient techniques for solving this task in recent years is the vision transformer technique. However, roads typically exhibit features such as uneven scales and low signal-to-noise ratios, which can be understood as the asymmetry between the road and the background category and the asymmetry in the transverse and longitudinal shape of the road. Existing vision transformer models, due to their fixed sliding window mechanism, cannot adapt to the uneven scale issue of roads. Additionally, self-attention, based on fully connected mechanisms for long sequences, may suffer from attention deviation due to excessive noise, making it unsuitable for low signal-to-noise ratio scenarios in road segmentation, resulting in incomplete and fragmented road segmentation results. In this paper, we propose a road extraction based on deformable self-attention computation, termed DOCswin-Trans (Deformable and Overlapped Cross-Window Transformer), to solve these problems. On the one hand, we develop a DOC-Transformer block to address the scale imbalance issue, which can utilize the overlapped window strategy to preserve the overall contextual semantic information of roads as much as possible. On the other hand, we propose a deformable window strategy to adaptively resample input vectors, which can direct attention automatically to the foreground areas relevant to roads and thereby address the low signal-to-noise ratio problem. We evaluate the proposed method on two popular road extraction datasets (i.e., DeepGlobe and Massachusetts datasets). The experimental results demonstrate that the proposed method outperforms baseline methods. On the DeepGlobe dataset, the proposed method achieves an IoU improvement ranging from 0.63% to 5.01% compared to baseline methods. On the Massachusetts dataset, our method achieves an IoU improvement ranging from 0.50% to 6.24% compared to baseline methods.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/sym16040468
- https://www.mdpi.com/2073-8994/16/4/468/pdf?version=1712898645
- OA Status
- gold
- Cited By
- 6
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394763572
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4394763572Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/sym16040468Digital Object Identifier
- Title
-
Road Extraction Method of Remote Sensing Image Based on Deformable Attention TransformerWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-04-12Full publication date if available
- Authors
-
Ling Zhao, Jianing Zhang, Xiujun Meng, Wenming Zhou, Zhenshi Zhang, Chengli PengList of authors in order
- Landing page
-
https://doi.org/10.3390/sym16040468Publisher landing page
- PDF URL
-
https://www.mdpi.com/2073-8994/16/4/468/pdf?version=1712898645Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2073-8994/16/4/468/pdf?version=1712898645Direct OA link when available
- Concepts
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Computer science, Segmentation, Transformer, Sliding window protocol, Artificial intelligence, Computer vision, Computation, Image segmentation, Pattern recognition (psychology), Window (computing), Algorithm, Operating system, Quantum mechanics, Voltage, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 1Per-year citation counts (last 5 years)
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40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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