Ship detection from optical satellite images based on visual search mechanism

Automatic ship detection from high-resolution optical satellite images has attracted great interest in the wide applications of maritime security and traffic control. However, most of the popular methods have much difficulty in extracting targets without false alarms due to the variable appearances of ships and complicated background. In this paper, we propose a ship detection approach based on visual search mechanism to solve this problem. First, salient regions are extracted by a global contrast model fast and easily. Second, geometric properties and neighborhood similarity of targets are used for discriminating the ship candidates with ambiguous appearance effectively. Furthermore, we utilize the SVM algorithm to classify each image as including target(s) or not according to the LBP feature of each ship candidate. Extensive experiments validate our proposed scheme outperforms the state-of-the-art methods in terms of detection time and accuracy.