Ship Detection in Range-Compressed SAR Data

Most of synthetic aperture radar (SAR) based ship detection methods utilize two-dimension focused images. Ship detection in range-compressed data is promising since it needs no time-consuming azimuth focusing. This paper proposes a ship detection method in range-compressed SAR data, which employs the statistical characteristics and range trajectory of a ship target in the range-compressed time-domain. First, it employs complex signal kurtosis (CSK) to prescreen potential ship areas since CSK was demonstrated to be an reasonable indicator for SAR ship detection. Then, a convolutional neural networks (CNN) based discrimination is applied to the potential ship areas. The training samples comes from the simulation results of range trajectory based on the radar imaging parameters. Preliminary results show that the proposed method performs well in range-compressed SAR data.