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

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 l...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2019-04, Vol.11 (7), p.765
Main Authors: Wang, Yuanyuan, Wang, Chao, Zhang, Hong, Dong, Yingbo, Wei, Sisi
Format: Article
Language:English
Subjects:
complex backgrounds
Datasets
Deep learning
Detectors
Image processing
Image segmentation
International conferences
Machine learning
Multimedia
object detectors
Pattern recognition
Radar imaging
SAR dataset
Satellite imagery
Sensors
ship detection
Surveillance
Synthetic aperture radar
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs11070765