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.

Due to multipath (MP) scattering, many special phenomena and details about the targets in high-resolution synthetic aperture radar (SAR) images are hard to understand. In this letter, a more elaborate improved model combined with the synthetic aperture progress is presented, to analyze the MP scattering. We explore the defocusing and displacement phenomena of MP scattering through signal modeling and time-frequency relationship analysis. The theoretical equations for the displacement of double scattering are derived, and the equation of phase error which causes the defocusing is also given. The SAR raw data simulation of MP scattering and range-Doppler (RD) imaging results validates the correctness of the analysis. From the equations, the effects of MP scattering of different height and different orientation angle in high-resolution SAR images are given. Finally, based on the simulation results, the new interpretation about the relationship between the moustache effect in high-resolution TerraSAR-X SAR images and the height of the structure is obtained.