Non-Cooperative Ship Target Fusion-Based Recognition with Deep Learning
- Resource Type
- Conference
- Authors
- Duan, Jiacheng; Han, Deqiang; Li, Wei
- Source
- 2023 42nd Chinese Control Conference (CCC) Chinese Control Conference (CCC), 2023 42nd. :3439-3443 Jul, 2023
- Subject
- Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Metalearning
Deep learning
Uncertainty
Target recognition
Diversity reception
Reconnaissance
Diversity methods
SAR Recognition
Deep Learning
Fusion Recognition
Meta-learning
- Language
- ISSN
- 1934-1768
Non-cooperative ship targets plays an important role in modern naval warfare recognition and reconnaissance. In the complicated environment, information obtained from a single source often has a high degree of uncertainty. By combining the information obtained from different sources, a more accurate recognition of the target can be expected. In this paper, we propose a fusion-based recognition method with deep learning. We build multiple classifiers based on handcraft features and deep features respectively. A meta-learning method is used to implement multiple classifiers fusion. Our experimental results show that the proposed method provides better recognition accuracies for non-cooperative ship targets in complex environments compared with single classifiers and traditional voting methods when performing multiple-class classification.