Inshore Ship Detection via Saliency and Context Information in High-Resolution SAR Images

Abstract

So far, the detection of ships on sea has been widely studied, while fewer works are available on inshore ship detection. Due to the high similarity between the harbor and the ship body on gray and texture features, the traditional methods are unable to achieve effective detection of inshore ships. In this letter, we present a novel approach via saliency and context information to deal with this issue. First, we employ the superpixel-generating algorithm to generate the superpixel region. Second, we propose a new method based on the salient region detection to complete the inshore ship detection. Finally, a ship discrimination framework is presented to remove the false alarm. The experimental results demonstrate the noise robustness and effectiveness of the proposed approach using synthetic aperture radar (SAR) images and real SAR images.