A Visual Search Inspired Computational Model for Ship Detection in Optical Satellite Images

Abstract

In this letter, we propose a novel computational model for automatic ship detection in optical satellite images. The model first selects salient candidate regions across entire detection scene by using a bottom-up visual attention mechanism. Then, two complementary types of top-down cues are employed to discriminate the selected ship candidates. Specifically, in addition to the detailed appearance analysis of candidates, a neighborhood similarity-based method is further exploited to characterize their local context interactions. Furthermore, the framework of our model is designed in a multiscale and hierarchical manner which provides a plausible approximation to a visual search process and reasonably distributes the computational resources. Experiments over panchromatic SPOT5 data prove the effectiveness and computational efficiency of the proposed model.