A Complex Background SAR Ship Target Detection Method Based on Fusion Tensor and Cross-Domain Adversarial Learning
Synthetic Aperture Radar (SAR) ship target detection has been extensively researched. However, most methods use the same dataset division for both training and validation. In practical applications, it is often necessary to quickly adapt to new loads, new modes, and new data to detect targets effect...
Saved in:
| Main Authors: | Haopeng Chan, Xiaolan Qiu, Xin Gao, Dongdong Lu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-09-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/16/18/3492 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Burned Area Mapping Using SAR and Multispectral Data Integration via Generative Adversarial Networks
by: Donato Amitrano
Published: (2025-01-01) -
SAR-SPA: Incorporating Target Scattering Characteristic Parameters in Adversarial Example Generation for SAR Imagery
by: Jiahao Cui, et al.
Published: (2025-01-01) -
Unsupervised Domain Adaptation for SAR Ship Detection Based on Multitask Decoupling
by: Yirong Yang, et al.
Published: (2025-01-01) -
Enhancing Few-Shot SAR Ship Recognition: Pseudospectrum Information Generation and Fusion
by: Gui Gao, et al.
Published: (2025-01-01) -
Adversarial example generation method for SAR images based on mask extraction
by: ZHANG Jianwu, et al.
Published: (2024-03-01)