SinGAN-Labeler: An Enhanced SinGAN for Generating Marine Oil Spill SAR Images with Labels
Deep learning-based SAR oil spill detection faces significant challenges due to limited labeled training data. To address this, we propose SinGAN-Labeler, an enhanced framework that generates high-quality synthetic SAR oil spill images and their labels from minimal input. The model integrates an ada...
Saved in:
| Main Authors: | Bin Wang, Lei Chen, Dongmei Song, Weimin Chen, Jintao Yu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/13/3/422 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
SAR Image Super-Resolution Based on Multiscale Edge Texture-Oriented GAN Approach
by: Yunfei Zhu, et al.
Published: (2025-01-01) -
Coffee plant disease identification with an attentive multi-image segmentation framework (MISF) with CycleGAN
by: Savitri Kulkarni, et al.
Published: (2025-06-01) -
Oil spill classification based on satellite image using deep learning techniques
by: Abubakar Salihu Abba, et al.
Published: (2024-02-01) -
Deep-Learning Methods for Defect Inspection of Plated Through Holes With Clustering-Based Auto-Labeling and GAN-Based Model Training
by: Chang-Yeh Hsieh, et al.
Published: (2024-01-01) -
Underwater image dehazing using a hybrid GAN with bottleneck attention and improved Retinex-based optimization
by: Amandeep Kaur, et al.
Published: (2025-07-01)