Dataset Editors: Tan Xiangdong; Leng Xiangguang; Ji kefeng; Kuang Gangyao

Timely monitoring of ships is imperative for ensuring the safety and security of maritime operations. Ship detection in synthetic aperture radar (SAR) is typically applicable to focused images. The time consumption of target detection primarily relies on the imaging process duration, encompassing intricate and time-intensive processing steps such as range migration correction and azimuth compression. Consequently, achieving real-time SAR ship detection poses a significant challenge. To address these issues, ship detection in the range-compressed domain of SAR has emerged as a viable approach. However, there is still a lack of reliable ship detection datasets that can satisfy the detection on the range-compressed domain. In this paper, we construct a dataset specifically designed for ship detection in range-compressed SAR data, called RCShip-1.0 (range-compressed ship dataset). The original data source is publicly available complex-valued data from the Sentinel-1 acquisition and the OpenSARShip-1.0 dataset, encompassing numerous ship targets. Subsequently, the inverse chirp scaling (ICS) algorithm is employed on the complex-valued data to acquire range-compressed SAR data. RCShip-1.0 encompasses training set, validation set, and test set acquired through two distinct approaches. It consists of 1580 large-scale SAR range-compressed images which are further divided into 18322 sub-images to facilitate subsequent display and analysis of detection results within large-scale SAR images. Experimental results obtained using the RCShip-1.0 dataset demonstrate its feasibility, standardization and public availability. The dataset will facilitate scholars in conducting comprehensive research on methodologies for ship detection using range-compressed SAR data.

 For details of parameters of MV3DSAR, please refer to RCShip-1.0：A Dataset Dedicated to Ship Detection in Range-Compressed SAR Data dataset usage Instructions.pdf

The current data reference format is as follows:

[1] X. Tan, X. Leng, K. Ji and G. Kuang. RCShip: A Dataset Dedicated to Ship Detection in  Range-Compressed SAR Data[J]. IEEE Geoscience and Remote Sensing Letters, vol. 21, pp. 1-5,  2024, Art no. 4004805, doi: 10.1109/LGRS.2024.3366749. 

[2] X. Tan, X. Leng, R. Luo, Z. Sun, K. Ji and G. Kuang. YOLO-RC: SAR Ship Detection  Guided by Characteristics of Range-Compressed Domain[J]. IEEE Journal of Selected Topics in  Applied Earth Observations and Remote Sensing, vol. 17, pp. 18834-18851, 2024, doi:  10.1109/JSTARS.2024.3478390. 

[3] 冷祥光, 谭向东, 计科峰, 匡纲要. SAR 距离压缩域舰船目标检测数据集（RCShip-1.0） [OL]. 雷达学报, 2025. https://radars.ac.cn/web/data/getData?newsColumnId=3ba34ef9-045e- 4453-b918-2650dfa3cfd1.

Xiangguang Leng, Xiangdong Tan, Kefeng Ji, Gangyao Kuang. RCShip: A Dataset Dedicated to  Ship Detection in Range-Compressed SAR Data (RCShip-1.0) [OL]. Journal of Radars, 2025. https://radars.ac.cn/web/data/getData?dataType=SARMultidomainShipDetectionDatasetEN&pageType=en

Data release time：2025-08-01