A Lightweight and Real-Time Dual-Polarization Fusion Framework for SAR Ship Classification.
Saved in:
| Title: | A Lightweight and Real-Time Dual-Polarization Fusion Framework for SAR Ship Classification. |
|---|---|
| Authors: | Gărăiman, Enrico1 (AUTHOR) florian.garaiman@upb.ro, Radoi, Anamaria1 (AUTHOR) |
| Source: | Remote Sensing. Apr2026, Vol. 18 Issue 8, p1129. 23p. |
| Subjects: | Linear polarization, Deep learning, Coastal surveillance, Computer performance, Real-time computing |
| Abstract: | Highlights: What are the main findings? A parallel dual-branch hybrid convolution-transformer architecture improves SAR ship classification performance, achieving high performance in terms of accuracy, i.e., the proposed technique achieves 97.50% accuracy in the 3-class configuration and 93.28% in the 6-class configuration scenarios on the OpenSARShip 2.0 dataset. Integrating dual-polarization modes strengthens class discrimination while maintaining computational efficiency. What are the implications of the main findings? Polarization-aware hybrid architectures provide a structured way to integrate dual-polarization information in SAR image analysis. The proposed design enables high classification accuracy without excessive computational cost, facilitating operational and real-time deployment. Synthetic Aperture Radar (SAR) ship classification plays a critical role in maritime surveillance, addressing challenges such as the similarity between ship categories, as well as scarcity of annotated datasets and data imbalance. In this paper, a lightweight and real-time dual-branch architecture is proposed to effectively address the SAR ship classification task. The proposed approach integrates dual-polarization data within a hybrid convolution-transformer framework to improve classification performance. The model fuses dual-polarization modes, combining convolutional layers for local feature extraction with transformer blocks for global contextual understanding. Evaluations on the OpenSARShip 2.0 dataset show that the proposed model achieves 97.50% accuracy in the 3-class configuration and 93.28% in the 6-class configuration. For the FUSAR-Ship dataset, which does not provide dual-polarization data for the same ship target, the single branch model achieved an accuracy of 94.92% for the 7-class configuration. Despite its dual-branch design, the model maintains computational efficiency, making it suitable for real-time maritime monitoring applications. The results demonstrate the effectiveness of polarization-aware hybrid models for scalable and robust SAR ship classification. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Highlights: What are the main findings? A parallel dual-branch hybrid convolution-transformer architecture improves SAR ship classification performance, achieving high performance in terms of accuracy, i.e., the proposed technique achieves 97.50% accuracy in the 3-class configuration and 93.28% in the 6-class configuration scenarios on the OpenSARShip 2.0 dataset. Integrating dual-polarization modes strengthens class discrimination while maintaining computational efficiency. What are the implications of the main findings? Polarization-aware hybrid architectures provide a structured way to integrate dual-polarization information in SAR image analysis. The proposed design enables high classification accuracy without excessive computational cost, facilitating operational and real-time deployment. Synthetic Aperture Radar (SAR) ship classification plays a critical role in maritime surveillance, addressing challenges such as the similarity between ship categories, as well as scarcity of annotated datasets and data imbalance. In this paper, a lightweight and real-time dual-branch architecture is proposed to effectively address the SAR ship classification task. The proposed approach integrates dual-polarization data within a hybrid convolution-transformer framework to improve classification performance. The model fuses dual-polarization modes, combining convolutional layers for local feature extraction with transformer blocks for global contextual understanding. Evaluations on the OpenSARShip 2.0 dataset show that the proposed model achieves 97.50% accuracy in the 3-class configuration and 93.28% in the 6-class configuration. For the FUSAR-Ship dataset, which does not provide dual-polarization data for the same ship target, the single branch model achieved an accuracy of 94.92% for the 7-class configuration. Despite its dual-branch design, the model maintains computational efficiency, making it suitable for real-time maritime monitoring applications. The results demonstrate the effectiveness of polarization-aware hybrid models for scalable and robust SAR ship classification. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 20724292 |
| DOI: | 10.3390/rs18081129 |
