Multiscale Feature-Based Infrared Ship Detection

Abstract

In this paper, based on the idea of “step-by-step accuracy”, a novel multiscale feature-based infrared ship-detection method (MSFISD) is proposed. The proposed method can achieve efficient and effective infrared ship detection in complex scenarios, which may provide assistance in applications such as night surveillance. First, candidate regions (CRs) are extracted from the whole image by extracting the sea–sky line and region of interest (ROI). The real sea–sky line is extracted based on the gradient features enhanced by large-scale gradient operators. The coarse segmentation results are obtained by the optimization method and are then refined by incorporating the edge features of the ship to reduce false alarms and obtain the CRs. Second, by analyzing the shape features of ships, the feature quantity is established, and the ships in CRs are finally accurately segmented. Experimental results demonstrate that compared with the other five methods, the proposed method has higher detection accuracy with a lower false-alarm rate and performs better in complex sea scenarios.