Abstract
Deep learning has been widely used in the field of SAR ship detection. However, current SAR ship detection still faces many challenges, such as complex scenes, multiple scales, and small targets. In order to promote the solution to the above problems, this article releases a high-resolution SAR ship detection dataset which can be used for rotating frame target detection. The dataset contains six categories of ships. In total, 30 panoramic SAR tiles of the Chinese Gaofen-3 of port areas with a 1-m resolution were cropped to slices, each with 1024 × 1024 pixels. In addition, most of the images in the dataset contain nearshore areas with complex background interference. Eight state-of-the-art rotated detectors and a CFAR-based method were used to evaluate the dataset. Experimental results revealed that the complex background will have a great impact on the performance of detectors.
Highlights
Research into Synthetic aperture radar (SAR) ship detection methods based on deep learning has made great progress
We found the corresponding area on the optical image according to the SAR image
The region proposal network (RPN) still uses horizontal boxes preliminary filtering, which has the advantage that it can speed up the training and testing for preliminary filtering, which has the advantage that it can speed up the training and of the algorithm to a certain extent
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Synthetic aperture radar (SAR) has been widely used in various fields due to its ability to acquire high-resolution images most the time and in all weather conditions. With the development of high-resolution spaceborne SAR, high-resolution SAR data are becoming more abundant and easier to acquire. As one of the significant ocean applications of SAR images, ship detection plays an important role in shipwreck rescues, maritime traffic safety, and so on
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