A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds

TLDR

Synthetic aperture radar images are increasingly available, yet the scarcity of large labeled datasets hampers the rapid development of deep‑learning ship detectors. The authors created a new SAR ship dataset to accelerate detector development. The dataset contains 43,819 256‑pixel ship chips from 210 SAR images (Gaofen‑3 and Sentinel‑1) and serves as a benchmark for training modified natural‑image detectors. Using the dataset, detectors achieved higher mAP and strong generalization to unseen SAR imagery, confirming its value.

Abstract

With the launch of space-borne satellites, more synthetic aperture radar (SAR) images are available than ever before, thus making dynamic ship monitoring possible. Object detectors in deep learning achieve top performance, benefitting from a free public dataset. Unfortunately, due to the lack of a large volume of labeled datasets, object detectors for SAR ship detection have developed slowly. To boost the development of object detectors in SAR images, a SAR dataset is constructed. This dataset labeled by SAR experts was created using 102 Chinese Gaofen-3 images and 108 Sentinel-1 images. It consists of 43,819 ship chips of 256 pixels in both range and azimuth. These ships mainly have distinct scales and backgrounds. Moreover, modified state-of-the-art object detectors from natural images are trained and can be used as baselines. Experimental results reveal that object detectors achieve higher mean average precision (mAP) on the test dataset and have high generalization performance on new SAR imagery without land-ocean segmentation, demonstrating the benefits of the dataset we constructed.