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

By Yuanyuan Wang, Chao Wang, Hong Zhang, Yin Dong, Sisi Wei. Remote Sensing, 2019

465 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/47643d5e70a4c8e266f47c1b87997b55768722d2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Richard Bamler
2. Xiaoxiang Zhu
3. Jun Shi
4. Feng Xu
5. Shunjun Wei
6. Lichao Mou

In Computer Vision and Pattern Recognition

1. Lichao Mou
2. Xiaoxiang Zhu
3. Hao Su
4. Jun Shi
5. Qizhe Qu
6. Mou Wang
7. Richard Bamler
8. Xiangfeng Zeng

Citations in the corpus

• HRSID: A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation (2020, methodology): “As for the SAR-Ship-Dataset, the small size ship chips are beneficial to ship classification; but they are incompetent to delicate ship detection tasks.”
• Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives (2020, methodology): “SAR-Ship-Dataset10 [196] 43,819 Gaofen-3 or Sentinel-1 image chips of different ships.”