A Benchmark Dataset for Fine-Grained Object Detection and Recognition Based on Single-Look Complex SAR Images (FAIR-CSAR-V1.0)

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.

FAIR-CSAR-V1.0数据集基于高分三号（Gaofen-3）卫星的单视复型（single-look complex, SLC）影像构建，是目前公开的规模最大、标注最精细的细粒度合成孔径雷达（SAR）图像数据集。本数据集旨在推动SAR图像目标检测、识别与目标特性理解相关技术的发展，由中国科学院空天信息创新研究院目标认知与应用技术重点实验室（Target Cognition and Application Technology, TCAT）研发。
FAIR-CSAR-V1.0包含175景高分三号一级SLC产品，覆盖全球32个区域，涵盖机场、炼油厂、港口、河流等典型地物。数据集总数据量达250 GB，实例总数超过34万个，涵盖5大类、22个子类，并提供了成像参数（如雷达中心频率、脉冲重复频率）与目标特性（如星地相对方位角、关键散射点分布）的详细标注。
FAIR-CSAR-V1.0包含SL与FSI两个子数据集。其中SL数据集采用聚束模式采集，标称分辨率为1米，包含22个目标类别的17万个实例；FSI数据集采用精细条带模式采集，标称分辨率为5米，包含3个目标类别的17万个实例。图1展示了本数据集的整体概览。
相关数据论文及引用格式如下：
[1] 吴有明, 刁文辉, 索钰曦, 孙显. 面向细粒度目标检测与识别的单视复型SAR图像基准数据集FAIR-CSAR-V1.0[OL]. 雷达学报, 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.

搜集汇总

背景与挑战

背景概述

FAIR-CSAR-V1.0是一个基于高分三号卫星单视复合成像（SLC）的合成孔径雷达（SAR）图像数据集，专为细粒度目标检测和识别而设计，是目前最大且注释最精细的SAR图像基准数据集。它包含175个场景，覆盖全球32个区域，总数据量250 GB，超过340,000个实例，涵盖5个主要类别和22个子类别，并提供详细的成像参数和目标特征注释。数据集分为SL和FSI两个子集，分别具有1米和5米分辨率，支持SAR图像对象检测、识别和目标特征理解等技术的进步。