Construction and Recognition Performance Analysis of Wide-swath SAR Maritime Large Moving Ships Dataset

Abstract

Wide-swath Synthetic Aperture Radar (SAR), represented by TopSAR and ScanSAR acquisition modes, can observe a vast area of ocean scenes. However, achieving wide-swath reduces the quality of imaging resolution, which causes the ships captured in wide-swath SAR images to not have clear structural characteristics. This phenomenon brings a great challenge to the identification of large maritime ships. Further, the lack of wide-swath SAR sample data of large critical ships, such as moving aircraft carriers and amphibious ships, makes the identification of maritime moving ships difficult. To solve this problem, we construct a wide-swath SAR large maritime moving ships dataset, which includes 2291 samples. The dataset is divided into the following categories: large military ships, large civilian ships of lengths greater than 250 m, and large civilian ships of lengths between 150～250 m. The construction process of the dataset is as follows: first, the sample data of large military ships in the port area are obtained from prior knowledge; second, the sample data of large civilian ships are obtained via the length screening of OpenSARShip dataset with attribute information; finally, the imaging results of moving ships at sea are simulated by adding quadratic phase error in a range-Doppler domain. This study also analyzes the recognition performance of the constructed dataset and motion simulation of the processed data using classical recognition algorithms and deep learning methods. Experimental results show that using SAR image complex information at low resolution can improve the recognition rate of the algorithm to a certain extent, and the defocusing problem of the moving ship target has a considerable impact on the recognition accuracy.

Keywords

• synthetic aperture radar (sar)
• wide-swath
• motion simulation
• large ship