Unsupervised Domain Adaptation for Cross-domain Remote Sensing Object Detection Via Joint Input and Feature Space Article Swipe
Deliang Chen
,
Taotao Cheng
,
Siyu Hong
,
Xilin Chen
,
Zixuan Lu
,
Yang Liu
,
Chen Ji
,
Liang Cheng
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-6642304/v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-6642304/v1
The rapid advancement of deep learning has led to significant achievements in remote sensing object detection. However, domain shift often causes notable performance drops when models trained on one domain are applied to real-world scenarios. Unsupervised domain adaptation (UDA) offers a solution by narrowing domain gaps. Generative adversarial networks (GANs) are commonly used for this purpose, but they can degrade key textures and details in source images. To address this, we propose a method that integrates transformations in both input and feature spaces. First, we standardize image dimensions across source and target domains. Then, a Joint Color Space Transformation (JCST) module operates in the feature space to decouple and recombine color channels, preserving crucial image details while aligning data distributions. We validated our approach on a dataset containing large-, medium-, and small-scale objects, using multiple object detection models. Results show that our method boosts average detection accuracy by 2–4% on source domain images, demonstrating improved generalization and robustness in cross-domain tasks.
Related Topics
Concepts
Joint (building)
Domain adaptation
Computer science
Domain (mathematical analysis)
Adaptation (eye)
Feature (linguistics)
Space (punctuation)
Artificial intelligence
Object (grammar)
Object detection
Pattern recognition (psychology)
Computer vision
Feature vector
Mathematics
Engineering
Psychology
Operating system
Linguistics
Classifier (UML)
Mathematical analysis
Architectural engineering
Philosophy
Neuroscience
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-6642304/v1
- https://www.researchsquare.com/article/rs-6642304/latest.pdf
- OA Status
- gold
- References
- 16
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4410906176
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4410906176Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-6642304/v1Digital Object Identifier
- Title
-
Unsupervised Domain Adaptation for Cross-domain Remote Sensing Object Detection Via Joint Input and Feature SpaceWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-05-30Full publication date if available
- Authors
-
Deliang Chen, Taotao Cheng, Siyu Hong, Xilin Chen, Zixuan Lu, Yang Liu, Chen Ji, Liang ChengList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-6642304/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-6642304/latest.pdfDirect 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.researchsquare.com/article/rs-6642304/latest.pdfDirect OA link when available
- Concepts
-
Joint (building), Domain adaptation, Computer science, Domain (mathematical analysis), Adaptation (eye), Feature (linguistics), Space (punctuation), Artificial intelligence, Object (grammar), Object detection, Pattern recognition (psychology), Computer vision, Feature vector, Mathematics, Engineering, Psychology, Operating system, Linguistics, Classifier (UML), Mathematical analysis, Architectural engineering, Philosophy, NeuroscienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
16Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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