Siamese-GAN: Learning Invariant Representations for Aerial Vehicle Image Categorization
Open accessAbstract
In this paper, we present a new algorithm for cross-domain classification in aerial vehicle images based on generative adversarial networks (GANs). The proposed method, called Siamese-GAN, learns invariant feature representations for both labeled and unlabeled images coming from two different domains. To this end, we train in an adversarial manner a Siamese encoder–decoder architecture coupled with a discriminator network. The encoder–decoder network has the task of matching the distributions of both domains in a shared space regularized by the reconstruction ability, while the discriminator seeks to distinguish between them. After this phase, we feed the resulting encoded labeled and unlabeled features to another network composed of two fully-connected layers for training and classification, respectively. Experiments on several cross-domain datasets composed of extremely high resolution (EHR) images acquired by manned/unmanned aerial vehicles (MAV/UAV) over the cities of Vaihingen, Toronto, Potsdam, and Trento are reported and discussed.
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Most UDA techniques in HAR work between domains that share the same feature space.
“An increasing number of UDA techniques have been applied in HAR; however, most of them work between domains that share the same feature space; for example, transferring the learn models between users [44] or between sensor wearing positions [32].”
From ContrasGAN: Unsupervised domain adaptation in Human Activity Recognition via adversarial and contrastive learning (2021) · cites this paper as [44]
We follow state-of-the-art UDA evaluation methodology by performing stratified split on target domain data into 60% training, 20% validation, and 20% testing.
“For each run, we follow the state-of-the-art UDA evaluation methodology [9,44,54] by performing stratified split on the target domain data into 60% for training, 20% for validation, and 20% for testing.”
From ContrasGAN: Unsupervised domain adaptation in Human Activity Recognition via adversarial and contrastive learning (2021) · cites this paper as [44]
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References 53 · Cited by 60
Research works printed but not linked: 3
- Mirza, M.; Osindero, S. Conditional Generative Adversarial Nets. Available online: https://arxiv.org/abs/ 1411.1784 (accessed on 23 February 2018).
- Li, J.; Skinner, K.A.; Eustice, R.M.; Johnson-Roberson, M. WaterGAN: Unsupervised generative network to enable real-time color correction of monocular underwater images. IEEE Robot. Autom. Lett. 2018, 3, 387-394. [CrossRef] · DOI
- Liu, M.Y.; Tuzel, O. Coupled Generative Adversarial Networks. Available online: https://arxiv.org/abs/ 1606.07536 (accessed on 23 February 2018).
- Authors
- Bashmal, L., Bazi, Y., AlHichri, H., AlRahhal, M., Ammour, N., & Alajlan, N.
- Affiliations
- King Saud University (SA)
- Information Science Department, College of Applied Computer Science, King Saud University, Riyadh 11543, Saudi Arabia
- Corresponding author
- Bazi, Yakoub
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2018-02-24
- Volume
- 10
- Issue
- 2
- Pages
- 351
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W2793797551
- Cited by
- 60
- References
- 53
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 1 · 2023: 5 · 2022: 7 · 2021: 14 · 2020: 15 · 2019: 11 · 2018: 1
- FWCI
- 5.74
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Remote Sensing (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Deanship of Scientific Research at King Saud University through the Local Research Group Program Under Project (RG-1435-055)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- cross-domain classification, generative adversarial networks, domain-invariant features, encoder-decoder architecture, unlabeled images, unmanned aerial vehicles, image classification, Siamese network
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