Gaussian-Driven Unsupervised Domain Adaptation Object Detection Transformer for Remote Sensing Imagery Article Swipe
Liang Chen
,
Shuang Song
,
Yupei Wang
,
Yongkang Hu
,
Jianhong Han
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2025.3608554
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2025.3608554
Domain shift can cause the detector to fail in adapting to the test data, resulting in sharp decline in detection performance. To address this issue, many unsupervised domain adaptation detection methods have been proposed to improve model’s generalization ability, which are typically designed for natural scene data with simple backgrounds, where the intraclass features of the objects often follow a simple unimodal distribution. However, remote sensing images usually have complex background interference, exacerbating the domain shift. In addition, the object class features in these images often exhibit multimodal distributions, making it difficult to effectively perform domain alignment within the same class. To overcome these challenges, we propose a DETR-based Gaussian-driven UDA method, named G-UDA. First, we introduce the real-time Gaussian frequency domain augmentation module, which increases the diversity of source domain data by perturbing the background information in specific frequency bands, while preserving object features. Next, we design the Gaussian class prototype alignment module based on Gaussian mixture model, which generates multiple Gaussian class prototypes, enabling the model to capture multimodal distribution within classes, thereby improving the representational capacity of intraclass features and enhancing interclass discriminability. Finally, we integrate our foreground focus alignment module with the teacher–student self-training framework. By replacing the traditional global feature alignment with focus on precisely aligning features of foreground objects, we facilitate the extraction of more robust object features by the detector under complex background interference. Experimental results in multiple remote sensing domain adaptation scenarios demonstrate that our method outperforms current state-of-the-art methods.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/jstars.2025.3608554
- OA Status
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- 47
- OpenAlex ID
- https://openalex.org/W4414221788
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https://openalex.org/W4414221788Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/jstars.2025.3608554Digital Object Identifier
- Title
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Gaussian-Driven Unsupervised Domain Adaptation Object Detection Transformer for Remote Sensing ImageryWork title
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articleOpenAlex work type
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enPrimary language
- Publication year
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2025Year of publication
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2025-01-01Full publication date if available
- Authors
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Liang Chen, Shuang Song, Yupei Wang, Yongkang Hu, Jianhong HanList of authors in order
- Landing page
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https://doi.org/10.1109/jstars.2025.3608554Publisher landing page
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/jstars.2025.3608554Direct OA link when available
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0Total citation count in OpenAlex
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47Number of works referenced by this work
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| abstract_inverted_index.methods. | 247 |
| abstract_inverted_index.multiple | 161, 234 |
| abstract_inverted_index.objects, | 214 |
| abstract_inverted_index.overcome | 102 |
| abstract_inverted_index.proposed | 33 |
| abstract_inverted_index.specific | 138 |
| abstract_inverted_index.unimodal | 61 |
| abstract_inverted_index.addition, | 77 |
| abstract_inverted_index.alignment | 96, 152, 192, 205 |
| abstract_inverted_index.detection | 19, 29 |
| abstract_inverted_index.difficult | 91 |
| abstract_inverted_index.diversity | 127 |
| abstract_inverted_index.enhancing | 183 |
| abstract_inverted_index.features. | 144 |
| abstract_inverted_index.frequency | 120, 139 |
| abstract_inverted_index.generates | 160 |
| abstract_inverted_index.improving | 175 |
| abstract_inverted_index.increases | 125 |
| abstract_inverted_index.integrate | 188 |
| abstract_inverted_index.introduce | 116 |
| abstract_inverted_index.precisely | 209 |
| abstract_inverted_index.prototype | 151 |
| abstract_inverted_index.real-time | 118 |
| abstract_inverted_index.replacing | 200 |
| abstract_inverted_index.resulting | 14 |
| abstract_inverted_index.scenarios | 239 |
| abstract_inverted_index.typically | 41 |
| abstract_inverted_index.DETR-based | 108 |
| abstract_inverted_index.adaptation | 28, 238 |
| abstract_inverted_index.background | 70, 135, 229 |
| abstract_inverted_index.extraction | 218 |
| abstract_inverted_index.facilitate | 216 |
| abstract_inverted_index.foreground | 190, 213 |
| abstract_inverted_index.framework. | 198 |
| abstract_inverted_index.interclass | 184 |
| abstract_inverted_index.intraclass | 52, 180 |
| abstract_inverted_index.multimodal | 87, 170 |
| abstract_inverted_index.perturbing | 133 |
| abstract_inverted_index.preserving | 142 |
| abstract_inverted_index.challenges, | 104 |
| abstract_inverted_index.demonstrate | 240 |
| abstract_inverted_index.effectively | 93 |
| abstract_inverted_index.information | 136 |
| abstract_inverted_index.outperforms | 244 |
| abstract_inverted_index.prototypes, | 164 |
| abstract_inverted_index.traditional | 202 |
| abstract_inverted_index.Experimental | 231 |
| abstract_inverted_index.augmentation | 122 |
| abstract_inverted_index.backgrounds, | 49 |
| abstract_inverted_index.distribution | 171 |
| abstract_inverted_index.exacerbating | 72 |
| abstract_inverted_index.performance. | 20 |
| abstract_inverted_index.unsupervised | 26 |
| abstract_inverted_index.distribution. | 62 |
| abstract_inverted_index.interference, | 71 |
| abstract_inverted_index.interference. | 230 |
| abstract_inverted_index.self-training | 197 |
| abstract_inverted_index.distributions, | 88 |
| abstract_inverted_index.generalization | 37 |
| abstract_inverted_index.model’s | 36 |
| abstract_inverted_index.Gaussian-driven | 109 |
| abstract_inverted_index.representational | 177 |
| abstract_inverted_index.state-of-the-art | 246 |
| abstract_inverted_index.discriminability. | 185 |
| abstract_inverted_index.teacher–student | 196 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.57431548 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |