MS-Net: A Multi-modal Self-supervised Network for Fine-Grained Classification of Aircraft in SAR Images Article Swipe
Bingying Yue
,
Jianhao Li
,
Hao Shi
,
Yupei Wang
,
Honghu Zhong
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.14613
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.14613
Synthetic aperture radar (SAR) imaging technology is commonly used to provide 24-hour all-weather earth observation. However, it still has some drawbacks in SAR target classification, especially in fine-grained classification of aircraft: aircrafts in SAR images have large intra-class diversity and inter-class similarity; the number of effective samples is insufficient and it's hard to annotate. To address these issues, this article proposes a novel multi-modal self-supervised network (MS-Net) for fine-grained classification of aircraft. Firstly, in order to entirely exploit the potential of multi-modal information, a two-sided path feature extraction network (TSFE-N) is constructed to enhance the image feature of the target and obtain the domain knowledge feature of text mode. Secondly, a contrastive self-supervised learning (CSSL) framework is employed to effectively learn useful label-independent feature from unbalanced data, a similarity per-ception loss (SPloss) is proposed to avoid network overfitting. Finally, TSFE-N is used as the encoder of CSSL to obtain the classification results. Through a large number of experiments, our MS-Net can effectively reduce the difficulty of classifying similar types of aircrafts. In the case of no label, the proposed algorithm achieves an accuracy of 88.46% for 17 types of air-craft classification task, which has pioneering significance in the field of fine-grained classification of aircraft in SAR images.
Related Topics
Concepts
Computer science
Overfitting
Artificial intelligence
Pattern recognition (psychology)
Feature (linguistics)
Similarity (geometry)
Feature extraction
Contextual image classification
Modal
Field (mathematics)
Class (philosophy)
Net (polyhedron)
Image (mathematics)
Artificial neural network
Mathematics
Chemistry
Pure mathematics
Linguistics
Geometry
Philosophy
Polymer chemistry
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2308.14613
- https://arxiv.org/pdf/2308.14613
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386273251
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386273251Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2308.14613Digital Object Identifier
- Title
-
MS-Net: A Multi-modal Self-supervised Network for Fine-Grained Classification of Aircraft in SAR ImagesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-28Full publication date if available
- Authors
-
Bingying Yue, Jianhao Li, Hao Shi, Yupei Wang, Honghu ZhongList of authors in order
- Landing page
-
https://arxiv.org/abs/2308.14613Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2308.14613Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2308.14613Direct OA link when available
- Concepts
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Computer science, Overfitting, Artificial intelligence, Pattern recognition (psychology), Feature (linguistics), Similarity (geometry), Feature extraction, Contextual image classification, Modal, Field (mathematics), Class (philosophy), Net (polyhedron), Image (mathematics), Artificial neural network, Mathematics, Chemistry, Pure mathematics, Linguistics, Geometry, Philosophy, Polymer chemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.learn | 120 |
| abstract_inverted_index.mode. | 108 |
| abstract_inverted_index.novel | 62 |
| abstract_inverted_index.order | 74 |
| abstract_inverted_index.radar | 2 |
| abstract_inverted_index.still | 17 |
| abstract_inverted_index.task, | 191 |
| abstract_inverted_index.these | 56 |
| abstract_inverted_index.types | 168, 187 |
| abstract_inverted_index.which | 192 |
| abstract_inverted_index.(CSSL) | 114 |
| abstract_inverted_index.88.46% | 184 |
| abstract_inverted_index.MS-Net | 159 |
| abstract_inverted_index.TSFE-N | 139 |
| abstract_inverted_index.domain | 103 |
| abstract_inverted_index.images | 34 |
| abstract_inverted_index.label, | 176 |
| abstract_inverted_index.number | 43, 155 |
| abstract_inverted_index.obtain | 101, 148 |
| abstract_inverted_index.reduce | 162 |
| abstract_inverted_index.target | 23, 99 |
| abstract_inverted_index.useful | 121 |
| abstract_inverted_index.24-hour | 11 |
| abstract_inverted_index.Through | 152 |
| abstract_inverted_index.address | 55 |
| abstract_inverted_index.article | 59 |
| abstract_inverted_index.encoder | 144 |
| abstract_inverted_index.enhance | 93 |
| abstract_inverted_index.exploit | 77 |
| abstract_inverted_index.feature | 86, 96, 105, 123 |
| abstract_inverted_index.images. | 206 |
| abstract_inverted_index.imaging | 4 |
| abstract_inverted_index.issues, | 57 |
| abstract_inverted_index.network | 65, 88, 136 |
| abstract_inverted_index.provide | 10 |
| abstract_inverted_index.samples | 46 |
| abstract_inverted_index.similar | 167 |
| abstract_inverted_index.(MS-Net) | 66 |
| abstract_inverted_index.(SPloss) | 131 |
| abstract_inverted_index.(TSFE-N) | 89 |
| abstract_inverted_index.Finally, | 138 |
| abstract_inverted_index.Firstly, | 72 |
| abstract_inverted_index.However, | 15 |
| abstract_inverted_index.accuracy | 182 |
| abstract_inverted_index.achieves | 180 |
| abstract_inverted_index.aircraft | 203 |
| abstract_inverted_index.aperture | 1 |
| abstract_inverted_index.commonly | 7 |
| abstract_inverted_index.employed | 117 |
| abstract_inverted_index.entirely | 76 |
| abstract_inverted_index.learning | 113 |
| abstract_inverted_index.proposed | 133, 178 |
| abstract_inverted_index.proposes | 60 |
| abstract_inverted_index.results. | 151 |
| abstract_inverted_index.Secondly, | 109 |
| abstract_inverted_index.Synthetic | 0 |
| abstract_inverted_index.air-craft | 189 |
| abstract_inverted_index.aircraft. | 71 |
| abstract_inverted_index.aircraft: | 30 |
| abstract_inverted_index.aircrafts | 31 |
| abstract_inverted_index.algorithm | 179 |
| abstract_inverted_index.annotate. | 53 |
| abstract_inverted_index.diversity | 38 |
| abstract_inverted_index.drawbacks | 20 |
| abstract_inverted_index.effective | 45 |
| abstract_inverted_index.framework | 115 |
| abstract_inverted_index.knowledge | 104 |
| abstract_inverted_index.potential | 79 |
| abstract_inverted_index.two-sided | 84 |
| abstract_inverted_index.aircrafts. | 170 |
| abstract_inverted_index.difficulty | 164 |
| abstract_inverted_index.especially | 25 |
| abstract_inverted_index.extraction | 87 |
| abstract_inverted_index.pioneering | 194 |
| abstract_inverted_index.similarity | 128 |
| abstract_inverted_index.technology | 5 |
| abstract_inverted_index.unbalanced | 125 |
| abstract_inverted_index.all-weather | 12 |
| abstract_inverted_index.classifying | 166 |
| abstract_inverted_index.constructed | 91 |
| abstract_inverted_index.contrastive | 111 |
| abstract_inverted_index.effectively | 119, 161 |
| abstract_inverted_index.inter-class | 40 |
| abstract_inverted_index.intra-class | 37 |
| abstract_inverted_index.multi-modal | 63, 81 |
| abstract_inverted_index.per-ception | 129 |
| abstract_inverted_index.similarity; | 41 |
| abstract_inverted_index.experiments, | 157 |
| abstract_inverted_index.fine-grained | 27, 68, 200 |
| abstract_inverted_index.information, | 82 |
| abstract_inverted_index.insufficient | 48 |
| abstract_inverted_index.observation. | 14 |
| abstract_inverted_index.overfitting. | 137 |
| abstract_inverted_index.significance | 195 |
| abstract_inverted_index.classification | 28, 69, 150, 190, 201 |
| abstract_inverted_index.classification, | 24 |
| abstract_inverted_index.self-supervised | 64, 112 |
| abstract_inverted_index.label-independent | 122 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |