RCShip: A Dataset Dedicated to Ship Detection in Range-Compressed SAR Data

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. The experimental results demonstrate that each deep network achieves good performance on the dataset, with an F 1 -score exceeding 65%. The utilization of the RCShip-1.0 dataset in obtaining these experimental outcomes showcases its feasibility, standardization, and public availability.