Abstract:
In complex and dynamic synthetic aperture radar (SAR) scenes, few-shot detection of novel classes suffers from sample scarcity and significant distribution differences be...Show MoreMetadata
Abstract:
In complex and dynamic synthetic aperture radar (SAR) scenes, few-shot detection of novel classes suffers from sample scarcity and significant distribution differences between base and novel class features, leading to severe bias and poor generalization in existing few-shot object detection (FSOD) models. To address this issue, we propose a meta-transfer learning method based on dynamic semantic guidance (DSG). This approach combines the strengths of meta-learning and transfer learning, comprising three modules: semantic guidance (SG), distribution alignment metric (DAM), and global feature dynamic aggregation (GFDA). The SG module generates guided features with query semantic information to reduce the distribution gap between base and novel classes, dynamically adapting to few-shot novel class SAR targets. The DAM module applies adversarial training to achieve dynamic feature distribution alignment, improving model bias and generalization. The GFDA module dynamically aggregates and retains critical feature information, enhancing model detection performance. Experimental results on the SRSDD-v1.0, MSAR-1.0, and SAR-AIRcraft-1.0 datasets show that the DSG method outperforms state-of-the-art methods in the SAR field [Gaussian metafeature balanced aggregation (GMFBA)] and the optical domain [generalized FSOD(G-FSOD)], with average detection performance improvements of 1.21%, 1.45%, 1.44%, and 9.76%, 2.86%, 1.8%, respectively.
Published in: IEEE Transactions on Geoscience and Remote Sensing ( Volume: 63)
Funding Agency:
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Synthetic Aperture Radar ,
- Synthetic Aperture Radar Target ,
- Meta-transfer Learning ,
- Distribution Characteristics ,
- Detection Performance ,
- Object Detection ,
- Global Features ,
- Transfer Learning ,
- Average Performance ,
- Detection Model ,
- Significant Difference In Distribution ,
- Aggregation Kinetics ,
- Dynamic Distribution ,
- Base Classes ,
- Optical Domain ,
- Improve Detection Performance ,
- Distribution Alignment ,
- Scarcity Of Samples ,
- Dynamic Alignment ,
- Classification Performance ,
- Query Features ,
- Synthetic Aperture Radar Images ,
- Generalization Capability ,
- Few-shot Learning ,
- Dynamic Update ,
- Target Detection Task ,
- Constant False Alarm Rate ,
- Aggregation Method ,
- Feature Aggregation ,
- Region Proposal Network
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Synthetic Aperture Radar ,
- Synthetic Aperture Radar Target ,
- Meta-transfer Learning ,
- Distribution Characteristics ,
- Detection Performance ,
- Object Detection ,
- Global Features ,
- Transfer Learning ,
- Average Performance ,
- Detection Model ,
- Significant Difference In Distribution ,
- Aggregation Kinetics ,
- Dynamic Distribution ,
- Base Classes ,
- Optical Domain ,
- Improve Detection Performance ,
- Distribution Alignment ,
- Scarcity Of Samples ,
- Dynamic Alignment ,
- Classification Performance ,
- Query Features ,
- Synthetic Aperture Radar Images ,
- Generalization Capability ,
- Few-shot Learning ,
- Dynamic Update ,
- Target Detection Task ,
- Constant False Alarm Rate ,
- Aggregation Method ,
- Feature Aggregation ,
- Region Proposal Network
- Author Keywords