Unsupervised Ship Detection for Single-Channel SAR Images Based on Multiscale Saliency and Complex Signal Kurtosis

Abstract

Traditional ship detection methods for synthetic aperture radar (SAR) mainly utilize the amplitude information to distinguish ship targets from sea clutter, including constant false alarm rate (CFAR), visual attention model, and deep learning methods. The CFAR algorithms adopt the dense sliding window strategy, which is very time-consuming and may generate numerous false alarms. The deep learning methods are supervised and difficult to obtain satisfactory performance when the number of labeled samples is insufficient. The visual attention models can quickly focus on the potential target area, and however, it is still difficult to eliminate the strong clutter, such as radio frequency interference and azimuth ambiguity. In fact, as a coherent imaging system, SAR data itself are complex-valued. Compared with the amplitude information, complex information can essentially reflect the difference between ship target and sea clutter. To improve the accuracy and efficiency of ship detection, in this letter, a novel unsupervised ship detection method based on multiscale saliency and complex signal kurtosis (MSS-CSK) for single-channel SAR images is proposed, which contains the proposal extraction stage and the target discrimination stage. The experimental results based on the Radarsat-2 real SAR data show that the proposed method has high detection accuracy and efficiency.