An Adaptive Sample Assignment Strategy Based on Feature Enhancement for Ship Detection in SAR Images Article Swipe
Hao Shi
,
Zhonghao Fang
,
Yupei Wang
,
Liang Chen
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14092238
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14092238
Recently, ship detection in synthetic aperture radar (SAR) images has received extensive attention. Most of the current ship detectors preset dense anchor boxes to achieve spatial alignment with ground-truth (GT) objects. Then, the detector defines the positive and negative samples based on the intersection-over-unit (IoU) between the anchors and GT objects. However, this label assignment strategy confuses the learning process of the model to a certain extent and results in suboptimal classification and regression results. In this paper, an adaptive sample assignment (ASA) strategy is proposed to select high-quality positive samples according to the spatial alignment and the knowledge learned from the regression and classification branches. Using our model, the selection of positive and negative samples is more explicit, which achieves better detection performance. A regression guided loss is proposed to further lead the detector to select well-classified and well-regressed anchors as high-quality positive samples by introducing the regression performance as a soft label in the calculation of the classification loss. In order to alleviate false alarms, a feature aggregation enhancement pyramid network (FAEPN) is proposed to enhance multi-scale feature representations and suppress the interference of background noise. Extensive experiments using the SAR ship detection dataset (SSDD) and high-resolution SAR images dataset (HRSID) demonstrate the superiority of our proposed approach.
Related Topics
Concepts
Computer science
Artificial intelligence
Pattern recognition (psychology)
Pyramid (geometry)
Synthetic aperture radar
Feature (linguistics)
Detector
Sample (material)
Regression
Intersection (aeronautics)
Mathematics
Statistics
Telecommunications
Philosophy
Chromatography
Engineering
Geometry
Aerospace engineering
Linguistics
Chemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs14092238
- https://www.mdpi.com/2072-4292/14/9/2238/pdf?version=1652327941
- OA Status
- gold
- Cited By
- 28
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4229373083
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4229373083Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs14092238Digital Object Identifier
- Title
-
An Adaptive Sample Assignment Strategy Based on Feature Enhancement for Ship Detection in SAR ImagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-05-07Full publication date if available
- Authors
-
Hao Shi, Zhonghao Fang, Yupei Wang, Liang ChenList of authors in order
- Landing page
-
https://doi.org/10.3390/rs14092238Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-4292/14/9/2238/pdf?version=1652327941Direct 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/2072-4292/14/9/2238/pdf?version=1652327941Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Pattern recognition (psychology), Pyramid (geometry), Synthetic aperture radar, Feature (linguistics), Detector, Sample (material), Regression, Intersection (aeronautics), Mathematics, Statistics, Telecommunications, Philosophy, Chromatography, Engineering, Geometry, Aerospace engineering, Linguistics, ChemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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28Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 9, 2023: 8, 2022: 6Per-year citation counts (last 5 years)
- References (count)
-
44Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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