Description

Automatic ship detection from optical satellite imagery is a challenging task due to cluttered scenes and variability in ship sizes. This letter proposes a detection algorithm based on saliency segmentation and the local binary pattern (LBP) descriptor combined with ship structure. First, we present a novel saliency segmentation framework with flexible integration of multiple visual cues to extract candidate regions from different sea surfaces. Then, simple shape analysis is adopted to eliminate obviously false targets. Finally, a structure-LBP feature that characterizes the inherent topology structure of ships is applied to discriminate true ship targets. Experimental results on numerous panchromatic satellite images validate that our proposed scheme outperforms other state-of-the-art methods in terms of both detection time and detection accuracy.