FSAR-Cap: A Fine-Grained Two-Stage Annotated Dataset for SAR Image Captioning
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908600723570688 |
|---|---|
| author | Zhang, Jinqi Zhang, Lamei Zou, Bin |
| author_facet | Zhang, Jinqi Zhang, Lamei Zou, Bin |
| contents | Synthetic Aperture Radar (SAR) image captioning enables scene-level semantic understanding and plays a crucial role in applications such as military intelligence and urban planning, but its development is limited by the scarcity of high-quality datasets. To address this, we present FSAR-Cap, a large-scale SAR captioning dataset with 14,480 images and 72,400 image-text pairs. FSAR-Cap is built on the FAIR-CSAR detection dataset and constructed through a two-stage annotation strategy that combines hierarchical template-based representation, manual verification and supplementation, prompt standardization. Compared with existing resources, FSAR-Cap provides richer fine-grained annotations, broader category coverage, and higher annotation quality. Benchmarking with multiple encoder-decoder architectures verifies its effectiveness, establishing a foundation for future research in SAR captioning and intelligent image interpretation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_16394 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FSAR-Cap: A Fine-Grained Two-Stage Annotated Dataset for SAR Image Captioning Zhang, Jinqi Zhang, Lamei Zou, Bin Image and Video Processing Synthetic Aperture Radar (SAR) image captioning enables scene-level semantic understanding and plays a crucial role in applications such as military intelligence and urban planning, but its development is limited by the scarcity of high-quality datasets. To address this, we present FSAR-Cap, a large-scale SAR captioning dataset with 14,480 images and 72,400 image-text pairs. FSAR-Cap is built on the FAIR-CSAR detection dataset and constructed through a two-stage annotation strategy that combines hierarchical template-based representation, manual verification and supplementation, prompt standardization. Compared with existing resources, FSAR-Cap provides richer fine-grained annotations, broader category coverage, and higher annotation quality. Benchmarking with multiple encoder-decoder architectures verifies its effectiveness, establishing a foundation for future research in SAR captioning and intelligent image interpretation. |
| title | FSAR-Cap: A Fine-Grained Two-Stage Annotated Dataset for SAR Image Captioning |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2510.16394 |