Unsupervised Ship Detection Based on Saliency and S-HOG Descriptor From Optical Satellite Images

With the development of high-resolution imagery, ship detection in optical satellite images has attracted a lot of research interest because of the broad applications in fishery management, vessel salvage, etc. Major challenges for this task include cloud, wave, and wake clutters, and even the variability of ship sizes. In this letter, we propose an unsupervised ship detection method toward overcoming these existing issues. Visual saliency, which focuses on highlighting salient signals from scenes, is applied to extract candidate regions followed by a homogeneous filter presented to confirm suspected ship targets with complete profiles. Then, a novel descriptor, ship histogram of oriented gradient, which characterizes the gradient symmetry of ship sides, is provided to discriminate real ships. Experimental results on numerous panchromatic satellite images demonstrate the good performance of our method compared to state-of-the-art methods.