DOI: 10.57760/sciencedb.radars.00019
title: A Benchmark Dataset for Fine-Grained Object Detection and Recognition Based on Single-Look Complex SAR Images (FAIR-CSAR-V1.0)
title: A Benchmark Dataset for Fine-Grained Object Detection and Recognition Based on Single-Look Complex SAR Images (FAIR-CSAR-V1.0)

description: FAIR-CSAR-V1.0 dataset, constructed on single-look complex (SLC) images of Gaofen-3 satellite, is the largest and most finely annotated SAR image dataset for fine-grained target to date. FAIR-CSAR-V1.0 aims to advance related technologies in SAR image object detection, recognition, and target characteristic understanding. The dataset is developed by Key Laboratory of Target Cognition and Application Technology (TCAT) at the Aerospace Information Research Institute, Chinese Academy of Sciences.FAIR-CSAR-V1.0 comprises 175 scenes of Gaofen-3 Level-1 SLC products, covering 32 global regions including airports, oil refineries, ports, and rivers. With a total data volume of 250 GB and over 340,000 instances, FAIR-CSAR-V1.0 covers 5 main categories and 22 subcategories, providing detailed annotations for imaging parameters (e.g., radar center frequency, pulse repetition frequency) and target characteristics (e.g., satellite-ground relative azimuthal angle, key scattering point distribution).FAIR-CSAR-V1.0 consists of two sub-datasets: the SL dataset and the FSI dataset. The SL dataset, acquired in spotlight mode with a nominal resolution of 1 meter, contains 170,000 instances across 22 target classes. The FSI dataset, acquired in fine stripmap mode with a nominal resolution of 5 meters, includes 170,000 instances across 3 target classes. Figure 1 presents an overview of the dataset.Data paper and citation format:[1] Youming Wu, Wenhui Diao, Yuxi Suo, Xian Sun. A Benchmark Dataset for Fine-Grained Object Detection and Recognition Based on Single-Look Complex SAR Images (FAIR-CSAR-V1.0) [OL]. Journal of Radars, 2025. https://radars.ac.cn/web/data/getData?dataType=FAIR_CSAR_en&pageType=en.[2] Y. Wu, Y. Suo, Q. Meng, W. Dai, T. Miao, W. Zhao, Z. Yan, W. Diao, G. Xie, Q. Ke, Y. Zhao, K. Fu and X. Sun, FAIR-CSAR: A Benchmark Dataset for Fine-Grained Object Detection and Recognition Based on Single-Look Complex SAR Images[J]. IEEE Transactions on Geoscience and Remote Sensing, vol. 63, pp. 1-22, 2025, doi: 10.1109/TGRS.2024.3519891.
description: FAIR-CSAR-V1.0 dataset, constructed on single-look complex (SLC) images of Gaofen-3 satellite, is the largest and most finely annotated SAR image dataset for fine-grained target to date. FAIR-CSAR-V1.0 aims to advance related technologies in SAR image object detection, recognition, and target characteristic understanding. The dataset is developed by Key Laboratory of Target Cognition and Application Technology (TCAT) at the Aerospace Information Research Institute, Chinese Academy of Sciences.FAIR-CSAR-V1.0 comprises 175 scenes of Gaofen-3 Level-1 SLC products, covering 32 global regions including airports, oil refineries, ports, and rivers. With a total data volume of 250 GB and over 340,000 instances, FAIR-CSAR-V1.0 covers 5 main categories and 22 subcategories, providing detailed annotations for imaging parameters (e.g., radar center frequency, pulse repetition frequency) and target characteristics (e.g., satellite-ground relative azimuthal angle, key scattering point distribution).FAIR-CSAR-V1.0 consists of two sub-datasets: the SL dataset and the FSI dataset. The SL dataset, acquired in spotlight mode with a nominal resolution of 1 meter, contains 170,000 instances across 22 target classes. The FSI dataset, acquired in fine stripmap mode with a nominal resolution of 5 meters, includes 170,000 instances across 3 target classes. Figure 1 presents an overview of the dataset.Data paper and citation format:[1] Youming Wu, Wenhui Diao, Yuxi Suo, Xian Sun. A Benchmark Dataset for Fine-Grained Object Detection and Recognition Based on Single-Look Complex SAR Images (FAIR-CSAR-V1.0) [OL]. Journal of Radars, 2025. https://radars.ac.cn/web/data/getData?dataType=FAIR_CSAR_en&pageType=en.[2] Y. Wu, Y. Suo, Q. Meng, W. Dai, T. Miao, W. Zhao, Z. Yan, W. Diao, G. Xie, Q. Ke, Y. Zhao, K. Fu and X. Sun, FAIR-CSAR: A Benchmark Dataset for Fine-Grained Object Detection and Recognition Based on Single-Look Complex SAR Images[J]. IEEE Transactions on Geoscience and Remote Sensing, vol. 63, pp. 1-22, 2025, doi: 10.1109/TGRS.2024.3519891.