Ship Detection in Complex Environment Using SAR Time Series

Abstract

Ship detection in complex environment is a challenging task due to strong background inferences, for which various deep-learning-based methods have been proposed. However, they have poor performance on detecting nearshore ships for medium-resolution synthetic aperture radar (SAR) images due to the loss of typical features and the confusion with the land scatterers. The availability of multitemporal SAR images gives the opportunity to separate nearshore ships with land scatterers by using the temporal characteristics. In this article, we propose a ship detection method based on SAR time series. First, we investigate the statistical stability of the SAR time series and propose a preclassification method to identify the potential changed pixel clusters. Then, we discriminate between ship and background pixel candidates in the preclassification by combining a rotating object detector and the transition detection algorithm and generate the corresponding frozen background reference (FBR) image. In addition, a dynamic framework for ship detection is proposed based on the FBR image and a two-stage outlier detection approach. The experiments show that the proposed method enables a dynamic ship monitoring with a high accuracy in ship detection and low false alarm rate for nearshore ship targets.