A Local-Sparse-Information-Aggregation Transformer with Explicit Contour Guidance for SAR Ship Detection Article Swipe
Hao Shi
,
Bingqian Chai
,
Yupei Wang
,
Liang Chen
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14205247
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14205247
Ship detection in synthetic aperture radar (SAR) images has witnessed rapid development in recent years, especially after the adoption of convolutional neural network (CNN)-based methods. Recently, a transformer using self-attention and a feed forward neural network with a encoder-decoder structure has received much attention from researchers, due to its intrinsic characteristics of global-relation modeling between pixels and an enlarged global receptive field. However, when adapting transformers to SAR ship detection, one challenging issue cannot be ignored. Background clutter, such as a coast, an island, or a sea wave, made previous object detectors easily miss ships with a blurred contour. Therefore, in this paper, we propose a local-sparse-information-aggregation transformer with explicit contour guidance for ship detection in SAR images. Based on the Swin Transformer architecture, in order to effectively aggregate sparse meaningful cues of small-scale ships, a deformable attention mechanism is incorporated to change the original self-attention mechanism. Moreover, a novel contour-guided shape-enhancement module is proposed to explicitly enforce the contour constraints on the one-dimensional transformer architecture. Experimental results show that our proposed method achieves superior performance on the challenging HRSID and SSDD datasets.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs14205247
- https://www.mdpi.com/2072-4292/14/20/5247/pdf?version=1666757388
- OA Status
- gold
- Cited By
- 19
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4306964296
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4306964296Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs14205247Digital Object Identifier
- Title
-
A Local-Sparse-Information-Aggregation Transformer with Explicit Contour Guidance for SAR Ship DetectionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-10-20Full publication date if available
- Authors
-
Hao Shi, Bingqian Chai, Yupei Wang, Liang ChenList of authors in order
- Landing page
-
https://doi.org/10.3390/rs14205247Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-4292/14/20/5247/pdf?version=1666757388Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2072-4292/14/20/5247/pdf?version=1666757388Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Synthetic aperture radar, Transformer, Clutter, Computer vision, Detector, Convolutional neural network, Encoder, Pattern recognition (psychology), Radar, Telecommunications, Physics, Quantum mechanics, Operating system, VoltageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
19Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 8, 2023: 7Per-year citation counts (last 5 years)
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60Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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