A model based hierarchical method for inshore ship detection in high-resolution remote sensing images

With the development of optical remote sensing satellite, ship detection and identification from large-scale remote sensing images has become a priority research topic. Specially, inshore ship detection has received increasing attention in many safe and marine applications. However, most of the popular techniques for inshore ship detection are limited by calculation efficiency and detection accuracy. In this paper, for inshore ship detection in complex harbor areas, we present a novel hierarchical method combining an efficient candidate scanning and a cascade model strategy. First, in the phase of candidate regions extraction, we design an omnidirectional intersected two-dimension (OITD) scanning method to extract candidate regions from the land-water segmented images rapidly. In addition, in candidate region identification phase, we structure a cascade model strategy to identify real ships from candidates to improve the accuracy of identification. The cascade model strategy is integrated by a bow model and a hull model of ship, which are trained by Deformable Part Model (DPM). Experiments on large-scale harbor remote sensing images show the higher precision and rapid computational efficiency of the proposed method.