Debiased Learning for Remote Sensing Data Article Swipe
Chun-Hsiao Yeh
,
Xudong Wang
,
Stella X. Yu
,
Charles Hill
,
Zackery Steck
,
Scott Kangas
,
Aaron Reite
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.15393
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.15393
Deep learning has had remarkable success at analyzing handheld imagery such as consumer photos due to the availability of large-scale human annotations (e.g., ImageNet). However, remote sensing data lacks such extensive annotation and thus potential for supervised learning. To address this, we propose a highly effective semi-supervised approach tailored specifically to remote sensing data. Our approach encompasses two key contributions. First, we adapt the FixMatch framework to remote sensing data by designing robust strong and weak augmentations suitable for this domain. Second, we develop an effective semi-supervised learning method by removing bias in imbalanced training data resulting from both actual labels and pseudo-labels predicted by the model. Our simple semi-supervised framework was validated by extensive experimentation. Using 30\% of labeled annotations, it delivers a 7.1\% accuracy gain over the supervised learning baseline and a 2.1\% gain over the supervised state-of-the-art CDS method on the remote sensing xView dataset.
Related Topics
Concepts
Computer science
Annotation
Key (lock)
Artificial intelligence
Labeled data
Supervised learning
Semi-supervised learning
Machine learning
Mobile device
Baseline (sea)
Domain (mathematical analysis)
Deep learning
World Wide Web
Artificial neural network
Mathematics
Oceanography
Computer security
Mathematical analysis
Geology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2312.15393
- https://arxiv.org/pdf/2312.15393
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390306293
All OpenAlex metadata
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https://openalex.org/W4390306293Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2312.15393Digital Object Identifier
- Title
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Debiased Learning for Remote Sensing DataWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-12-24Full publication date if available
- Authors
-
Chun-Hsiao Yeh, Xudong Wang, Stella X. Yu, Charles Hill, Zackery Steck, Scott Kangas, Aaron ReiteList of authors in order
- Landing page
-
https://arxiv.org/abs/2312.15393Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2312.15393Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2312.15393Direct OA link when available
- Concepts
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Computer science, Annotation, Key (lock), Artificial intelligence, Labeled data, Supervised learning, Semi-supervised learning, Machine learning, Mobile device, Baseline (sea), Domain (mathematical analysis), Deep learning, World Wide Web, Artificial neural network, Mathematics, Oceanography, Computer security, Mathematical analysis, GeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.imagery | 9 |
| abstract_inverted_index.labeled | 119 |
| abstract_inverted_index.propose | 42 |
| abstract_inverted_index.sensing | 26, 52, 68, 145 |
| abstract_inverted_index.success | 5 |
| abstract_inverted_index.FixMatch | 64 |
| abstract_inverted_index.However, | 24 |
| abstract_inverted_index.accuracy | 125 |
| abstract_inverted_index.approach | 47, 55 |
| abstract_inverted_index.baseline | 131 |
| abstract_inverted_index.consumer | 12 |
| abstract_inverted_index.dataset. | 147 |
| abstract_inverted_index.delivers | 122 |
| abstract_inverted_index.handheld | 8 |
| abstract_inverted_index.learning | 1, 87, 130 |
| abstract_inverted_index.removing | 90 |
| abstract_inverted_index.suitable | 77 |
| abstract_inverted_index.tailored | 48 |
| abstract_inverted_index.training | 94 |
| abstract_inverted_index.analyzing | 7 |
| abstract_inverted_index.designing | 71 |
| abstract_inverted_index.effective | 45, 85 |
| abstract_inverted_index.extensive | 30, 114 |
| abstract_inverted_index.framework | 65, 110 |
| abstract_inverted_index.learning. | 37 |
| abstract_inverted_index.potential | 34 |
| abstract_inverted_index.predicted | 103 |
| abstract_inverted_index.resulting | 96 |
| abstract_inverted_index.validated | 112 |
| abstract_inverted_index.ImageNet). | 23 |
| abstract_inverted_index.annotation | 31 |
| abstract_inverted_index.imbalanced | 93 |
| abstract_inverted_index.remarkable | 4 |
| abstract_inverted_index.supervised | 36, 129, 138 |
| abstract_inverted_index.annotations | 21 |
| abstract_inverted_index.encompasses | 56 |
| abstract_inverted_index.large-scale | 19 |
| abstract_inverted_index.annotations, | 120 |
| abstract_inverted_index.availability | 17 |
| abstract_inverted_index.specifically | 49 |
| abstract_inverted_index.augmentations | 76 |
| abstract_inverted_index.pseudo-labels | 102 |
| abstract_inverted_index.contributions. | 59 |
| abstract_inverted_index.semi-supervised | 46, 86, 109 |
| abstract_inverted_index.experimentation. | 115 |
| abstract_inverted_index.state-of-the-art | 139 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |